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ORCIDMihaela van der Schaar
Mihaela van der Schaar-Mitrea
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- affiliation: University of Cambridge, Department of Applied Mathematics and Theoretical Physics, UK
- affiliation: University of California, Los Angeles, Electrical Engineering Department, CA, USA
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2020 – today
- 2025
[c411]Krzysztof Kacprzyk, Mihaela van der Schaar:
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs. ICLR 2025- [c410]Kasia Kobalczyk, Nicolás Astorga, Tennison Liu, Mihaela van der Schaar:
Active Task Disambiguation with LLMs. ICLR 2025 - [c409]Kasia Kobalczyk, Mihaela van der Schaar:
Towards Automated Knowledge Integration From Human-Interpretable Representations. ICLR 2025 - [c408]Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar:
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples. ICLR 2025 - [c407]Tennison Liu, Nicolas Huynh, Mihaela van der Schaar:
Decision Tree Induction Through LLMs via Semantically-Aware Evolution. ICLR 2025 - [c406]Jiashuo Liu, Nabeel Seedat, Peng Cui, Mihaela van der Schaar:
Going Beyond Static: Understanding Shifts with Time-Series Attribution. ICLR 2025 - [i300]Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar:
Towards Human-Guided, Data-Centric LLM Co-Pilots. CoRR abs/2501.10321 (2025) - [i299]Krzysztof Kacprzyk, Mihaela van der Schaar:
No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs. CoRR abs/2501.18563 (2025) - [i298]Hao Sun, Yunyi Shen, Jean-Francois Ton, Mihaela van der Schaar:
Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUs. CoRR abs/2502.04357 (2025) - [i297]Katarzyna Kobalczyk, Nicolas Astorga, Tennison Liu, Mihaela van der Schaar:
Active Task Disambiguation with LLMs. CoRR abs/2502.04485 (2025) - [i296]Omer Noy Klein, Alihan Hüyük, Ron Shamir, Uri Shalit, Mihaela van der Schaar:
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions. CoRR abs/2503.09226 (2025) - [i295]Tennison Liu, Nicolas Huynh, Mihaela van der Schaar:
Decision Tree Induction Through LLMs via Semantically-Aware Evolution. CoRR abs/2503.14217 (2025) - 2024
- [j264]Elisabeth R. M. Heremans
, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos:
U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging. Comput. Biol. Medicine 171: 108205 (2024) - [j263]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality. Found. Trends Signal Process. 18(3): 200-309 (2024) - [j262]Lea Goetz, Nabeel Seedat, Robert Vandersluis, Mihaela van der Schaar:
Generalization - a key challenge for responsible AI in patient-facing clinical applications. npj Digit. Medicine 7(1) (2024) - [j261]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. IEEE Trans. Artif. Intell. 5(6): 2589-2603 (2024) - [j260]Alexis Bellot, Mihaela van der Schaar:
Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding. IEEE Trans. Neural Networks Learn. Syst. 35(4): 4948-4962 (2024) - [c405]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
Adaptive Experiment Design with Synthetic Controls. AISTATS 2024: 1180-1188 - [c404]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. AISTATS 2024: 1864-1872 - [c403]Krzysztof Kacprzyk, Mihaela van der Schaar:
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. AISTATS 2024: 3601-3609 - [c402]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. ECAI 2024: 2669-2676 - [c401]Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
A Neural Framework for Generalized Causal Sensitivity Analysis. ICLR 2024 - [c400]Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar:
L2MAC: Large Language Model Automatic Computer for Extensive Code Generation. ICLR 2024 - [c399]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. ICLR 2024 - [c398]Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. ICLR 2024 - [c397]Krzysztof Kacprzyk, Tennison Liu, Mihaela van der Schaar:
Towards Transparent Time Series Forecasting. ICLR 2024 - [c396]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. ICLR 2024 - [c395]Yangming Li, Mihaela van der Schaar:
On Error Propagation of Diffusion Models. ICLR 2024 - [c394]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. ICLR 2024 - [c393]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. ICLR 2024 - [c392]Hao Sun, Alihan Hüyük, Mihaela van der Schaar:
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL. ICLR 2024 - [c391]Boris van Breugel, Mihaela van der Schaar:
Position: Why Tabular Foundation Models Should Be a Research Priority. ICML 2024 - [c390]Alex James Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Dense Reward for Free in Reinforcement Learning from Human Feedback. ICML 2024 - [c389]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. ICML 2024 - [c388]Chohee Kim, Mihaela van der Schaar, Changhee Lee:
Discovering Features with Synergistic Interactions in Multiple Views. ICML 2024 - [c387]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. ICML 2024 - [c386]Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel:
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. ICML 2024 - [c385]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes. ICML 2024 - [c384]Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert T. Lange:
Discovering Preference Optimization Algorithms with and for Large Language Models. NeurIPS 2024 - [c383]Nicolás Astorga, Tennison Liu, Nabeel Seedat, Mihaela van der Schaar:
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios. NeurIPS 2024 - [c382]Samuel Holt, Tennison Liu, Mihaela van der Schaar:
Automatically Learning Hybrid Digital Twins of Dynamical Systems. NeurIPS 2024 - [c381]Samuel Holt, Zhaozhi Qian, Tennison Liu, James Weatherall, Mihaela van der Schaar:
Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models. NeurIPS 2024 - [c380]Alan Jeffares, Alicia Curth, Mihaela van der Schaar:
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. NeurIPS 2024 - [c379]Max Ruiz Luyten, Mihaela van der Schaar:
A theoretical design of concept sets: improving the predictability of concept bottleneck models. NeurIPS 2024 - [c378]Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner. NeurIPS 2024 - [c377]Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar:
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. NeurIPS 2024 - [c376]Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar:
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models. NeurIPS 2024 - [i294]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Deep Generative Symbolic Regression. CoRR abs/2401.00282 (2024) - [i293]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
Adaptive Experiment Design with Synthetic Controls. CoRR abs/2401.17205 (2024) - [i292]Alex J. Chan, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Dense Reward for Free in Reinforcement Learning from Human Feedback. CoRR abs/2402.00782 (2024) - [i291]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers. CoRR abs/2402.01502 (2024) - [i290]Yangming Li, Max Ruiz Luyten, Mihaela van der Schaar:
Risk-Sensitive Diffusion: Learning the Underlying Distribution from Noisy Samples. CoRR abs/2402.02081 (2024) - [i289]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. CoRR abs/2402.03921 (2024) - [i288]Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar:
Time Series Diffusion in the Frequency Domain. CoRR abs/2402.05933 (2024) - [i287]Thomas Pouplin, Hao Sun, Samuel Holt, Mihaela van der Schaar:
Retrieval-Augmented Thought Process as Sequential Decision Making. CoRR abs/2402.07812 (2024) - [i286]Katarzyna Kobalczyk, Mihaela van der Schaar:
Informed Meta-Learning. CoRR abs/2402.16105 (2024) - [i285]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. CoRR abs/2402.17599 (2024) - [i284]Alihan Hüyük, Qiyao Wei, Alicia Curth, Mihaela van der Schaar:
Defining Expertise: Applications to Treatment Effect Estimation. CoRR abs/2403.00694 (2024) - [i283]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. CoRR abs/2403.04551 (2024) - [i282]Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. CoRR abs/2403.10766 (2024) - [i281]Krzysztof Kacprzyk, Mihaela van der Schaar:
Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations. CoRR abs/2404.09788 (2024) - [i280]Boris van Breugel, Mihaela van der Schaar:
Why Tabular Foundation Models Should Be a Research Priority. CoRR abs/2405.01147 (2024) - [i279]Yangming Li, Mihaela van der Schaar:
A Study of Posterior Stability for Time-Series Latent Diffusion. CoRR abs/2405.14021 (2024) - [i278]Hao Sun, Mihaela van der Schaar:
Inverse-RLignment: Inverse Reinforcement Learning from Demonstrations for LLM Alignment. CoRR abs/2405.15624 (2024) - [i277]Jonas Schweisthal, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel:
Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments. CoRR abs/2406.02464 (2024) - [i276]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. CoRR abs/2406.03258 (2024) - [i275]Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob N. Foerster, Mihaela van der Schaar, Robert Tjarko Lange:
Discovering Preference Optimization Algorithms with and for Large Language Models. CoRR abs/2406.08414 (2024) - [i274]Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar:
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling. CoRR abs/2406.13733 (2024) - [i273]Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar:
LaTable: Towards Large Tabular Models. CoRR abs/2406.17673 (2024) - [i272]Fergus Imrie, Stefan Denner, Lucas S. Brunschwig, Klaus H. Maier-Hein, Mihaela van der Schaar:
Automated Ensemble Multimodal Machine Learning for Healthcare. CoRR abs/2407.18227 (2024) - [i271]Alexander Bastounis, Paolo Campodonico, Mihaela van der Schaar, Ben Adcock, Anders C. Hansen:
On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of 'I don't know'. CoRR abs/2408.02357 (2024) - [i270]Zahra Atashgahi, Tennison Liu, Mykola Pechenizkiy, Raymond N. J. Veldhuis, Decebal Constantin Mocanu, Mihaela van der Schaar:
Unveiling the Power of Sparse Neural Networks for Feature Selection. CoRR abs/2408.04583 (2024) - [i269]Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis, Mihaela van der Schaar:
CliMB: An AI-enabled Partner for Clinical Predictive Modeling. CoRR abs/2410.03736 (2024) - [i268]Stefan Feuerriegel, Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Konstantin Hess, Alicia Curth, Stefan Bauer, Niki Kilbertus, Isaac S. Kohane, Mihaela van der Schaar:
Causal machine learning for predicting treatment outcomes. CoRR abs/2410.08770 (2024) - [i267]Samuel Holt, Tennison Liu, Mihaela van der Schaar:
Automatically Learning Hybrid Digital Twins of Dynamical Systems. CoRR abs/2410.23691 (2024) - [i266]Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar:
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models. CoRR abs/2410.24005 (2024) - [i265]Nabeel Seedat, Mihaela van der Schaar:
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching. CoRR abs/2410.24105 (2024) - [i264]Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar:
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments. CoRR abs/2411.00186 (2024) - [i263]Alan Jeffares, Alicia Curth, Mihaela van der Schaar:
Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond. CoRR abs/2411.00247 (2024) - [i262]Nicolás Astorga, Tennison Liu, Yuanzhang Xiao, Mihaela van der Schaar:
Autoformulation of Mathematical Optimization Models Using LLMs. CoRR abs/2411.01679 (2024) - [i261]Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner. CoRR abs/2411.03387 (2024) - [i260]Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca, Mihaela van der Schaar, James Weatherall, Adam Taylor:
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding. CoRR abs/2411.13163 (2024) - [i259]Paulius Rauba, Qiyao Wei, Mihaela van der Schaar:
Quantifying perturbation impacts for large language models. CoRR abs/2412.00868 (2024) - [i258]Thomas Pouplin, Katarzyna Kobalczyk, Hao Sun, Mihaela van der Schaar:
LLMs for Generalizable Language-Conditioned Policy Learning under Minimal Data Requirements. CoRR abs/2412.06877 (2024) - [i257]Katarzyna Kobalczyk, Claudio Fanconi, Hao Sun, Mihaela van der Schaar:
Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes. CoRR abs/2412.13998 (2024) - 2023
- [j259]Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar:
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge. ACM Trans. Comput. Heal. 4(2): 15:1-15:29 (2023) - [j258]Fergus Imrie, Robert Davis, Mihaela van der Schaar:
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare. Nat. Mac. Intell. 5(8): 824-829 (2023) - [j257]Mahed Abroshan, Kai Hou Yip, Cem Tekin, Mihaela van der Schaar:
Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6368-6378 (2023) - [j256]Onur Atan, Saeed Ghoorchian, Setareh Maghsudi, Mihaela van der Schaar:
Data-Driven Online Recommender Systems With Costly Information Acquisition. IEEE Trans. Serv. Comput. 16(1): 235-245 (2023) - [c375]Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. AISTATS 2023: 1747-1778 - [c374]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. AISTATS 2023: 3466-3492 - [c373]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. AISTATS 2023: 3493-3514 - [c372]Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar:
To Impute or not to Impute? Missing Data in Treatment Effect Estimation. AISTATS 2023: 3568-3590 - [c371]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. AISTATS 2023: 7961-7980 - [c370]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. AISTATS 2023: 10160-10177 - [c369]Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. AISTATS 2023: 10279-10304 - [c368]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Deep Generative Symbolic Regression. ICLR 2023 - [c367]Alihan Hüyük, Zhaozhi Qian, Mihaela van der Schaar:
When to Make and Break Commitments? ICLR 2023 - [c366]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. ICLR 2023 - [c365]Tennison Liu, Zhaozhi Qian, Jeroen Berrevoets, Mihaela van der Schaar:
GOGGLE: Generative Modelling for Tabular Data by Learning Relational Structure. ICLR 2023 - [c364]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. ICML 2023: 2206-2233 - [c363]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions. ICML 2023: 6603-6622 - [c362]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. ICML 2023: 6623-6642 - [c361]Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. ICML 2023: 21388-21403 - [c360]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic Data, Real Errors: How (Not) to Publish and Use Synthetic Data. ICML 2023: 34793-34808 - [c359]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. ICML 2023: 34855-34874 - [c358]Jeroen Berrevoets, Daniel Jarrett, Alex J. Chan, Mihaela van der Schaar:
AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems. NeurIPS 2023 - [c357]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. NeurIPS 2023 - [c356]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. NeurIPS 2023 - [c355]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. NeurIPS 2023 - [c354]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. NeurIPS 2023 - [c353]Samuel Holt, Alihan Hüyük, Mihaela van der Schaar:
Active Observing in Continuous-time Control. NeurIPS 2023 - [c352]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. NeurIPS 2023 - [c351]Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
D-CIPHER: Discovery of Closed-form Partial Differential Equations. NeurIPS 2023 - [c350]Zhaozhi Qian, Robert Davis, Mihaela van der Schaar:
Synthcity: a benchmark framework for diverse use cases of tabular synthetic data. NeurIPS 2023 - [c349]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
Risk-Averse Active Sensing for Timely Outcome Prediction under Cost Pressure. NeurIPS 2023 - [c348]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. NeurIPS 2023 - [c347]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. NeurIPS 2023 - [c346]Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples. NeurIPS 2023 - [e3]Mihaela van der Schaar, Cheng Zhang, Dominik Janzing:
Conference on Causal Learning and Reasoning, CLeaR 2023, 11-14 April 2023, Amazon Development Center, Tübingen, Germany, April 11-14, 2023. Proceedings of Machine Learning Research 213, PMLR 2023 [contents] - [i256]Zhaozhi Qian, Bogdan-Constantin Cebere, Mihaela van der Schaar:
Synthcity: facilitating innovative use cases of synthetic data in different data modalities. CoRR abs/2301.07573 (2023) - [i255]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Mihaela van der Schaar:
Joint Training of Deep Ensembles Fails Due to Learner Collusion. CoRR abs/2301.11323 (2023) - [i254]Evgeny S. Saveliev, Mihaela van der Schaar:
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine. CoRR abs/2301.12260 (2023) - [i253]Alicia Curth, Mihaela van der Schaar:
In Search of Insights, Not Magic Bullets: Towards Demystification of the Model Selection Dilemma in Heterogeneous Treatment Effect Estimation. CoRR abs/2302.02923 (2023) - [i252]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. CoRR abs/2302.12238 (2023) - [i251]Boris van Breugel, Hao Sun, Zhaozhi Qian, Mihaela van der Schaar:
Membership Inference Attacks against Synthetic Data through Overfitting Detection. CoRR abs/2302.12580 (2023) - [i250]Samuel Holt, Alihan Hüyük, Zhaozhi Qian, Hao Sun, Mihaela van der Schaar:
Neural Laplace Control for Continuous-time Delayed Systems. CoRR abs/2302.12604 (2023) - [i249]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. CoRR abs/2302.12619 (2023) - [i248]Alicia Curth, Mihaela van der Schaar:
Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data. CoRR abs/2302.12718 (2023) - [i247]Alexander Norcliffe, Bogdan Cebere, Fergus Imrie, Pietro Liò, Mihaela van der Schaar:
SurvivalGAN: Generating Time-to-Event Data for Survival Analysis. CoRR abs/2302.12749 (2023) - [i246]Mahed Abroshan, Oscar Giles, Sam F. Greenbury, Jack Roberts, Mihaela van der Schaar, Jannetta S. Steyn, Alan Wilson, May Yong:
Learning machines for health and beyond. CoRR abs/2303.01513 (2023) - [i245]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning. CoRR abs/2303.02186 (2023) - [i244]Alan Jeffares, Tennison Liu, Jonathan Crabbé, Fergus Imrie, Mihaela van der Schaar:
TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization. CoRR abs/2303.05506 (2023) - [i243]Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz:
Machine Learning with Requirements: a Manifesto. CoRR abs/2304.03674 (2023) - [i242]Boris van Breugel, Mihaela van der Schaar:
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. CoRR abs/2304.03722 (2023) - [i241]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. CoRR abs/2304.06715 (2023) - [i240]Boris van Breugel, Zhaozhi Qian, Mihaela van der Schaar:
Synthetic data, real errors: how (not) to publish and use synthetic data. CoRR abs/2305.09235 (2023) - [i239]Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar:
Learning Representations without Compositional Assumptions. CoRR abs/2305.19726 (2023) - [i238]Toon Vanderschueren, Alicia Curth, Wouter Verbeke, Mihaela van der Schaar:
Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time. CoRR abs/2306.04255 (2023) - [i237]Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos:
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging. CoRR abs/2306.04663 (2023) - [i236]Aleksa Bisercic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Pietro Liò, Andrija Petrovic:
Interpretable Medical Diagnostics with Structured Data Extraction by Large Language Models. CoRR abs/2306.05052 (2023) - [i235]Yangming Li, Zhaozhi Qian, Mihaela van der Schaar:
Do Diffusion Models Suffer Error Propagation? Theoretical Analysis and Consistency Regularization. CoRR abs/2308.05021 (2023) - [i234]Hao Sun, Alihan Hüyük, Mihaela van der Schaar:
Query-Dependent Prompt Evaluation and Optimization with Offline Inverse RL. CoRR abs/2309.06553 (2023) - [i233]Yangming Li, Boris van Breugel, Mihaela van der Schaar:
Soft Mixture Denoising: Beyond the Expressive Bottleneck of Diffusion Models. CoRR abs/2309.14068 (2023) - [i232]Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar:
L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation. CoRR abs/2310.02003 (2023) - [i231]Fergus Imrie, Paulius Rauba, Mihaela van der Schaar:
Redefining Digital Health Interfaces with Large Language Models. CoRR abs/2310.03560 (2023) - [i230]Hao Sun, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples. CoRR abs/2310.07747 (2023) - [i229]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. CoRR abs/2310.16524 (2023) - [i228]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. CoRR abs/2310.16981 (2023) - [i227]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Inverse Decision Modeling: Learning Interpretable Representations of Behavior. CoRR abs/2310.18591 (2023) - [i226]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. CoRR abs/2310.18601 (2023) - [i225]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. CoRR abs/2310.18688 (2023) - [i224]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. CoRR abs/2310.18970 (2023) - [i223]Alicia Curth, Alan Jeffares, Mihaela van der Schaar:
A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning. CoRR abs/2310.18988 (2023) - [i222]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning. CoRR abs/2310.19831 (2023) - [i221]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Time-series Generation by Contrastive Imitation. CoRR abs/2311.01388 (2023) - [i220]Ioana Bica, Daniel Jarrett, Mihaela van der Schaar:
Invariant Causal Imitation Learning for Generalizable Policies. CoRR abs/2311.01489 (2023) - [i219]Alex J. Chan, Alihan Hüyük, Mihaela van der Schaar:
Optimising Human-AI Collaboration by Learning Convincing Explanations. CoRR abs/2311.07426 (2023) - [i218]Max Zhu, Katarzyna Kobalczyk, Andrija Petrovic, Mladen Nikolic, Mihaela van der Schaar, Boris Delibasic, Petro Liò:
Tabular Few-Shot Generalization Across Heterogeneous Feature Spaces. CoRR abs/2311.10051 (2023) - [i217]Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlas, Ahmed M. Alaa, Adji Bousso Dieng, Natasha F. Noy, Vijay Janapa Reddi, James Zou, Praveen K. Paritosh, Mihaela van der Schaar, Kurt D. Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson:
DMLR: Data-centric Machine Learning Research - Past, Present and Future. CoRR abs/2311.13028 (2023) - [i216]Hao Sun, Alex J. Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar:
When is Off-Policy Evaluation Useful? A Data-Centric Perspective. CoRR abs/2311.14110 (2023) - [i215]Dennis Frauen, Fergus Imrie, Alicia Curth, Valentyn Melnychuk, Stefan Feuerriegel, Mihaela van der Schaar:
A Neural Framework for Generalized Causal Sensitivity Analysis. CoRR abs/2311.16026 (2023) - [i214]Tim Schubert, Richard W. Peck, Alexander Gimson, Camelia Davtyan, Mihaela van der Schaar:
A Foundational Framework and Methodology for Personalized Early and Timely Diagnosis. CoRR abs/2311.16195 (2023) - [i213]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes. CoRR abs/2312.12112 (2023) - 2022
- [j255]Cong Shen, Zhaozhi Qian, Alihan Hüyük, Mihaela van der Schaar:
MARS: Assisting Human with Information Processing Tasks Using Machine Learning. ACM Trans. Comput. Heal. 3(2): 21:1-21:19 (2022) - [j254]Iacopo Vagliano, Sylvia Brinkman, Ameen Abu-Hanna, M. Sesmu Arbous, Dave A. Dongelmans, Paul W. G. Elbers, Dylan W. De Lange, Mihaela van der Schaar, Nicolette F. de Keizer, Martijn C. Schut:
Can we reliably automate clinical prognostic modelling? A retrospective cohort study for ICU triage prediction of in-hospital mortality of COVID-19 patients in the Netherlands. Int. J. Medical Informatics 160: 104688 (2022) - [j253]Changhee Lee, Alexander Light, Evgeny S. Saveliev, Mihaela van der Schaar, Vincent J. Gnanapragasam:
Developing machine learning algorithms for dynamic estimation of progression during active surveillance for prostate cancer. npj Digit. Medicine 5 (2022) - [j252]Cem Tekin, Sepehr Elahi, Mihaela van der Schaar:
Feedback Adaptive Learning for Medical and Educational Application Recommendation. IEEE Trans. Serv. Comput. 15(4): 2144-2157 (2022) - [c345]Alihan Hüyük, William R. Zame, Mihaela van der Schaar:
Inferring Lexicographically-Ordered Rewards from Preferences. AAAI 2022: 5737-5745 - [c344]Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar:
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. AISTATS 2022: 4158-4177 - [c343]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Neural graphical modelling in continuous-time: consistency guarantees and algorithms. ICLR 2022 - [c342]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR 2022 - [c341]Changhee Lee, Fergus Imrie, Mihaela van der Schaar:
Self-Supervision Enhanced Feature Selection with Correlated Gates. ICLR 2022 - [c340]Alizée Pace, Alex J. Chan, Mihaela van der Schaar:
POETREE: Interpretable Policy Learning with Adaptive Decision Trees. ICLR 2022 - [c339]Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar:
D-CODE: Discovering Closed-form ODEs from Observed Trajectories. ICLR 2022 - [c338]Ahmed M. Alaa, Boris van Breugel, Evgeny S. Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. ICML 2022: 290-306 - [c337]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. ICML 2022: 4391-4420 - [c336]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. ICML 2022: 8811-8832 - [c335]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Inverse Contextual Bandits: Learning How Behavior Evolves over Time. ICML 2022: 9506-9524 - [c334]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML 2022: 9916-9937 - [c333]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. ICML 2022: 19467-19496 - [c332]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. ICML 2022: 19497-19521 - [c331]Mihaela van der Schaar:
Machine Learning for Medicine and Healthcare. ICPRAM 2022: 5 - [c330]Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. NeurIPS 2022 - [c329]Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. NeurIPS 2022 - [c328]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS 2022 - [c327]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. NeurIPS 2022 - [c326]Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar:
Composite Feature Selection Using Deep Ensembles. NeurIPS 2022 - [c325]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. NeurIPS 2022 - [c324]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. NeurIPS 2022 - [e2]Isabelle Guyon, Marius Lindauer, Mihaela van der Schaar, Frank Hutter, Roman Garnett:
International Conference on Automated Machine Learning, AutoML 2022, 25-27 July 2022, Johns Hopkins University, Baltimore, MD, USA. Proceedings of Machine Learning Research 188, PMLR 2022 [contents] - [i212]Jeroen Berrevoets, Fergus Imrie, Trent Kyono, James Jordon, Mihaela van der Schaar:
To Impute or not to Impute? - Missing Data in Treatment Effect Estimation. CoRR abs/2202.02096 (2022) - [i211]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. CoRR abs/2202.08836 (2022) - [i210]Alihan Hüyük, William R. Zame, Mihaela van der Schaar:
Inferring Lexicographically-Ordered Rewards from Preferences. CoRR abs/2202.10153 (2022) - [i209]Tobias Hatt, Jeroen Berrevoets, Alicia Curth, Stefan Feuerriegel, Mihaela van der Schaar:
Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects. CoRR abs/2202.12891 (2022) - [i208]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. CoRR abs/2203.01928 (2022) - [i207]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. CoRR abs/2203.07338 (2022) - [i206]Alizée Pace, Alex J. Chan, Mihaela van der Schaar:
POETREE: Interpretable Policy Learning with Adaptive Decision Trees. CoRR abs/2203.08057 (2022) - [i205]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. CoRR abs/2206.04843 (2022) - [i204]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. CoRR abs/2206.06354 (2022) - [i203]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. CoRR abs/2206.07769 (2022) - [i202]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. CoRR abs/2206.08311 (2022) - [i201]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. CoRR abs/2206.08363 (2022) - [i200]Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
D-CIPHER: Discovery of Closed-form PDEs. CoRR abs/2206.10586 (2022) - [i199]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
DAUX: a Density-based Approach for Uncertainty eXplanations. CoRR abs/2207.05161 (2022) - [i198]Yanke Li, Tobias Hatt, Ioana Bica, Mihaela van der Schaar:
DAPDAG: Domain Adaptation via Perturbed DAG Reconstruction. CoRR abs/2208.01373 (2022) - [i197]Alicia Curth, Alihan Hüyük, Mihaela van der Schaar:
Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials. CoRR abs/2208.05844 (2022) - [i196]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. CoRR abs/2209.11222 (2022) - [i195]Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. CoRR abs/2210.05320 (2022) - [i194]Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. CoRR abs/2210.06183 (2022) - [i193]Fergus Imrie, Bogdan Cebere, Eoin F. McKinney, Mihaela van der Schaar:
AutoPrognosis 2.0: Democratizing Diagnostic and Prognostic Modeling in Healthcare with Automated Machine Learning. CoRR abs/2210.12090 (2022) - [i192]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. CoRR abs/2210.13043 (2022) - [i191]Fergus Imrie, Alexander Norcliffe, Pietro Liò, Mihaela van der Schaar:
Composite Feature Selection using Deep Ensembles. CoRR abs/2211.00631 (2022) - [i190]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems. CoRR abs/2211.05764 (2022) - [i189]Tennison Liu, Alex J. Chan, Boris van Breugel, Mihaela van der Schaar:
Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes. CoRR abs/2211.06138 (2022) - [i188]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Navigating causal deep learning. CoRR abs/2212.00911 (2022) - 2021
- [j251]Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar:
Triage of 2D Mammographic Images Using Multi-view Multi-task Convolutional Neural Networks. ACM Trans. Comput. Heal. 2(3): 26:1-26:24 (2021) - [j250]Mihaela van der Schaar, Ahmed M. Alaa, R. Andres Floto, Alexander Gimson, Stefan Scholtes, Angela M. Wood, Eoin F. McKinney, Daniel Jarrett, Pietro Lió, Ari Ercole:
How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Mach. Learn. 110(1): 1-14 (2021) - [j249]Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19. Mach. Learn. 110(1): 15-35 (2021) - [j248]Ahmed M. Alaa, Deepti Gurdasani, Adrian L. Harris, Jem Rashbass, Mihaela van der Schaar:
Machine learning to guide the use of adjuvant therapies for breast cancer. Nat. Mach. Intell. 3(8): 716-726 (2021) - [j247]Xingchi Liu, Mahsa Derakhshani, Sangarapillai Lambotharan, Mihaela van der Schaar:
Risk-Aware Multi-Armed Bandits With Refined Upper Confidence Bounds. IEEE Signal Process. Lett. 28: 269-273 (2021) - [j246]Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar:
Personalized Education in the Artificial Intelligence Era: What to Expect Next. IEEE Signal Process. Mag. 38(3): 37-50 (2021) - [j245]Trent Kyono, Mihaela van der Schaar:
Exploiting Causal Structure for Robust Model Selection in Unsupervised Domain Adaptation. IEEE Trans. Artif. Intell. 2(6): 494-507 (2021) - [j244]Changhee Lee, Jem Rashbass, Mihaela van der Schaar:
Outcome-Oriented Deep Temporal Phenotyping of Disease Progression. IEEE Trans. Biomed. Eng. 68(8): 2423-2434 (2021) - [j243]Yuanzhang Xiao, Mihaela van der Schaar:
Dynamic Stochastic Demand Response With Energy Storage. IEEE Trans. Smart Grid 12(6): 4813-4821 (2021) - [c323]Changhee Lee, Mihaela van der Schaar:
A Variational Information Bottleneck Approach to Multi-Omics Data Integration. AISTATS 2021: 1513-1521 - [c322]Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. AISTATS 2021: 1810-1818 - [c321]Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar:
Learning Matching Representations for Individualized Organ Transplantation Allocation. AISTATS 2021: 2134-2142 - [c320]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. AISTATS 2021: 2980-2988 - [c319]Mihaela van der Schaar:
Building next-generation healthcare systems using distributed machine learning. ICDCN 2021: 4 - [c318]Ahmed M. Alaa, Alex James Chan, Mihaela van der Schaar:
Generative Time-series Modeling with Fourier Flows. ICLR 2021 - [c317]Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Learning "What-if" Explanations for Sequential Decision-Making. ICLR 2021 - [c316]Alex James Chan, Mihaela van der Schaar:
Scalable Bayesian Inverse Reinforcement Learning. ICLR 2021 - [c315]Alihan Hüyük, Daniel Jarrett, Cem Tekin, Mihaela van der Schaar:
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning. ICLR 2021 - [c314]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. ICLR 2021 - [c313]Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. ICML 2021: 759-768 - [c312]Jeroen Berrevoets, Ahmed M. Alaa, Zhaozhi Qian, James Jordon, Alexander E. S. Gimson, Mihaela van der Schaar:
Learning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis. ICML 2021: 792-802 - [c311]Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. ICML 2021: 2166-2177 - [c310]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Inverse Decision Modeling: Learning Interpretable Representations of Behavior. ICML 2021: 4755-4771 - [c309]Ioana Bica, Daniel Jarrett, Mihaela van der Schaar:
Invariant Causal Imitation Learning for Generalizable Policies. NeurIPS 2021: 3952-3964 - [c308]Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar:
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. NeurIPS 2021: 22221-22233 - [c307]Alex J. Chan, Ioana Bica, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation. NeurIPS Datasets and Benchmarks 2021 - [c306]Alicia Curth, David Svensson, James Weatherall, Mihaela van der Schaar:
Really Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation. NeurIPS Datasets and Benchmarks 2021 - [c305]Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela M. Wood, Mihaela van der Schaar:
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes. NeurIPS 2021: 3178-3190 - [c304]Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar:
Conformal Time-series Forecasting. NeurIPS 2021: 6216-6228 - [c303]Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul W. G. Elbers, Mihaela van der Schaar:
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression. NeurIPS 2021: 11364-11383 - [c302]Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar:
Explaining Latent Representations with a Corpus of Examples. NeurIPS 2021: 12154-12166 - [c301]Alicia Curth, Mihaela van der Schaar:
On Inductive Biases for Heterogeneous Treatment Effect Estimation. NeurIPS 2021: 15883-15894 - [c300]Yuchao Qin, Fergus Imrie, Alihan Hüyük, Daniel Jarrett, Alexander Gimson, Mihaela van der Schaar:
Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation. NeurIPS 2021: 23205-23217 - [c299]Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar:
Estimating Multi-cause Treatment Effects via Single-cause Perturbation. NeurIPS 2021: 23754-23767 - [c298]Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. NeurIPS 2021: 23806-23817 - [c297]Alicia Curth, Changhee Lee, Mihaela van der Schaar:
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. NeurIPS 2021: 26740-26753 - [c296]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Time-series Generation by Contrastive Imitation. NeurIPS 2021: 28968-28982 - [c295]Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Preface: AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021. SPACA 2021: 1-2 - [c294]Alexis Bellot, Mihaela van der Schaar:
Application of kernel hypothesis testing on set-valued data. UAI 2021: 194-204 - [c293]Alexis Bellot, Mihaela van der Schaar:
A kernel two-sample test with selection bias. UAI 2021: 205-214 - [e1]Russell Greiner, Neeraj Kumar, Thomas Alexander Gerds, Mihaela van der Schaar:
Proceedings of AAAI Symposium on Survival Prediction - Algorithms, Challenges and Applications, SPACA 2021, Stanford University, Palo Alto, CA, USA, March 22-24, 2021. Proceedings of Machine Learning Research 146, PMLR 2021 [contents] - [i187]Setareh Maghsudi, Andrew S. Lan, Jie Xu, Mihaela van der Schaar:
Personalized Education in the AI Era: What to Expect Next? CoRR abs/2101.10074 (2021) - [i186]Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. CoRR abs/2101.10943 (2021) - [i185]Hyun-Suk Lee, Cong Shen, William R. Zame, Jang-Won Lee, Mihaela van der Schaar:
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups. CoRR abs/2101.10998 (2021) - [i184]Can Xu, Ahmed M. Alaa, Ioana Bica, Brent D. Ershoff, Maxime Cannesson, Mihaela van der Schaar:
Learning Matching Representations for Individualized Organ Transplantation Allocation. CoRR abs/2101.11769 (2021) - [i183]Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. CoRR abs/2102.01577 (2021) - [i182]Changhee Lee, Mihaela van der Schaar:
A Variational Information Bottleneck Approach to Multi-Omics Data Integration. CoRR abs/2102.03014 (2021) - [i181]Trent Kyono, Ioana Bica, Zhaozhi Qian, Mihaela van der Schaar:
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge. CoRR abs/2102.06271 (2021) - [i180]Alex J. Chan, Mihaela van der Schaar:
Scalable Bayesian Inverse Reinforcement Learning. CoRR abs/2102.06483 (2021) - [i179]Ahmed M. Alaa, Boris van Breugel, Evgeny Saveliev, Mihaela van der Schaar:
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models. CoRR abs/2102.08921 (2021) - [i178]Victor D. Bourgin, Ioana Bica, Mihaela van der Schaar:
Model-Attentive Ensemble Learning for Sequence Modeling. CoRR abs/2102.11500 (2021) - [i177]Alexis Bellot, Mihaela van der Schaar:
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding. CoRR abs/2103.15106 (2021) - [i176]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Consistency of mechanistic causal discovery in continuous-time using Neural ODEs. CoRR abs/2105.02522 (2021) - [i175]Zhaozhi Qian, William R. Zame, Lucas M. Fleuren, Paul W. G. Elbers, Mihaela van der Schaar:
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression. CoRR abs/2106.02875 (2021) - [i174]Alicia Curth, Mihaela van der Schaar:
On Inductive Biases for Heterogeneous Treatment Effect Estimation. CoRR abs/2106.03765 (2021) - [i173]Alex J. Chan, Ioana Bica, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation. CoRR abs/2106.04240 (2021) - [i172]Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. CoRR abs/2106.05303 (2021) - [i171]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Inverse Contextual Bandits: Learning How Behavior Evolves over Time. CoRR abs/2107.06317 (2021) - [i170]Alicia Curth, Mihaela van der Schaar:
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators. CoRR abs/2107.13346 (2021) - [i169]Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar:
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. CoRR abs/2108.03039 (2021) - [i168]Mahed Abroshan, Kai Hou Yip, Cem Tekin, Mihaela van der Schaar:
Conservative Policy Construction Using Variational Autoencoders for Logged Data with Missing Values. CoRR abs/2109.03747 (2021) - [i167]Boris van Breugel, Trent Kyono, Jeroen Berrevoets, Mihaela van der Schaar:
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks. CoRR abs/2110.12884 (2021) - [i166]Alicia Curth, Changhee Lee, Mihaela van der Schaar:
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. CoRR abs/2110.14001 (2021) - [i165]Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar:
Explaining Latent Representations with a Corpus of Examples. CoRR abs/2110.15355 (2021) - [i164]Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. CoRR abs/2111.03187 (2021) - [i163]Jeroen Berrevoets, Alicia Curth, Ioana Bica, Eoin F. McKinney, Mihaela van der Schaar:
Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time. CoRR abs/2112.03811 (2021) - 2020
- [j242]Alexis Bellot, Mihaela van der Schaar:
Flexible Modelling of Longitudinal Medical Data: A Bayesian Nonparametric Approach. ACM Trans. Comput. Heal. 1(1): 3:1-3:15 (2020) - [j241]Yangbo Song, Mihaela van der Schaar:
Dynamic network formation with foresighted agents. Int. J. Game Theory 49(2): 345-384 (2020) - [j240]Fergus Imrie, Anthony R. Bradley, Mihaela van der Schaar, Charlotte M. Deane:
Deep Generative Models for 3D Linker Design. J. Chem. Inf. Model. 60(4): 1983-1995 (2020) - [j239]Changhee Lee, Jinsung Yoon, Mihaela van der Schaar:
Dynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis With Competing Risks Based on Longitudinal Data. IEEE Trans. Biomed. Eng. 67(1): 122-133 (2020) - [j238]Kartik Ahuja, Mihaela van der Schaar:
Dynamic Matching and Allocation of Tasks. ACM Trans. Economics and Comput. 7(4): 19:1-19:27 (2020) - [j237]Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar:
Dynamic Prediction in Clinical Survival Analysis Using Temporal Convolutional Networks. IEEE J. Biomed. Health Informatics 24(2): 424-436 (2020) - [j236]Jinsung Yoon, Lydia N. Drumright, Mihaela van der Schaar:
Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE J. Biomed. Health Informatics 24(8): 2378-2388 (2020) - [j235]Jianwei Huang, Costas Courcoubetis, Mihaela van der Schaar, Biying Shou, Jean C. Walrand:
Guest Editorial: Introduction to the Special Section on Economics of Modern Networks. IEEE Trans. Netw. Sci. Eng. 7(2): 619-620 (2020) - [j234]Setareh Maghsudi, Mihaela van der Schaar:
A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching With Rateless-Coded Transmission. IEEE Trans. Wirel. Commun. 19(7): 5040-5056 (2020) - [c292]Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. AISTATS 2020: 1005-1014 - [c291]Yao Zhang, Daniel Jarrett, Mihaela van der Schaar:
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning. AISTATS 2020: 2304-2314 - [c290]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. AISTATS 2020: 2645-2654 - [c289]Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Mihaela van der Schaar, Jem Rashbass:
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. AISTATS 2020: 3295-3305 - [c288]Mihaela van der Schaar:
AutoML and Interpretability: Powering the Machine Learning Revolution in Healthcare. FODS 2020: 1 - [c287]Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar:
Estimating counterfactual treatment outcomes over time through adversarially balanced representations. ICLR 2020 - [c286]Daniel Jarrett, Mihaela van der Schaar:
Target-Embedding Autoencoders for Supervised Representation Learning. ICLR 2020 - [c285]Ahmed M. Alaa, Mihaela van der Schaar:
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions. ICML 2020: 165-174 - [c284]Ahmed M. Alaa, Mihaela van der Schaar:
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. ICML 2020: 175-190 - [c283]Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar:
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. ICML 2020: 884-895 - [c282]Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar:
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift. ICML 2020: 1392-1402 - [c281]Daniel Jarrett, Mihaela van der Schaar:
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making. ICML 2020: 4713-4723 - [c280]Changhee Lee, Mihaela van der Schaar:
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression. ICML 2020: 5767-5777 - [c279]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. ICML 2020: 8730-8740 - [c278]Jeroen Berrevoets, James Jordon, Ioana Bica, Alexander Gimson, Mihaela van der Schaar:
OrganITE: Optimal transplant donor organ offering using an individual treatment effect. NeurIPS 2020 - [c277]Ioana Bica, James Jordon, Mihaela van der Schaar:
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. NeurIPS 2020 - [c276]Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar:
Learning outside the Black-Box: The pursuit of interpretable models. NeurIPS 2020 - [c275]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Strictly Batch Imitation Learning by Energy-based Distribution Matching. NeurIPS 2020 - [c274]James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification. NeurIPS (Competition and Demos) 2020: 206-215 - [c273]Trent Kyono, Yao Zhang, Mihaela van der Schaar:
CASTLE: Regularization via Auxiliary Causal Graph Discovery. NeurIPS 2020 - [c272]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. NeurIPS 2020 - [c271]Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes. NeurIPS 2020 - [c270]Jinsung Yoon, Yao Zhang, James Jordon, Mihaela van der Schaar:
VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain. NeurIPS 2020 - [c269]Yao Zhang, Mihaela van der Schaar:
Gradient Regularized V-Learning for Dynamic Treatment Regimes. NeurIPS 2020 - [d1]Pedro Baqui, Ioana Bica, Valerio Marra, Ari Ercole, Mihaela van der Schaar:
ioanabica/COVID-19-Brazil: Code release. Zenodo, 2020
- [i162]Hyun-Suk Lee, Cong Shen, James Jordon, Mihaela van der Schaar:
Contextual Constrained Learning for Dose-Finding Clinical Trials. CoRR abs/2001.02463 (2020) - [i161]Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot, Jem Rashbass, Mihaela van der Schaar:
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes. CoRR abs/2001.02585 (2020) - [i160]Yao Zhang, Daniel Jarrett, Mihaela van der Schaar:
Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning. CoRR abs/2001.03898 (2020) - [i159]Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects. CoRR abs/2001.04754 (2020) - [i158]Daniel Jarrett, Mihaela van der Schaar:
Target-Embedding Autoencoders for Supervised Representation Learning. CoRR abs/2001.08345 (2020) - [i157]Ioana Bica, Ahmed M. Alaa, James Jordon, Mihaela van der Schaar:
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations. CoRR abs/2002.04083 (2020) - [i156]Ioana Bica, James Jordon, Mihaela van der Schaar:
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks. CoRR abs/2002.12326 (2020) - [i155]Setareh Maghsudi, Mihaela van der Schaar:
A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission. CoRR abs/2005.04154 (2020) - [i154]Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
When to Lift the Lockdown? Global COVID-19 Scenario Planning and Policy Effects using Compartmental Gaussian Processes. CoRR abs/2005.08837 (2020) - [i153]Cong Shen, Zhiyang Wang, Sofia S. Villar, Mihaela van der Schaar:
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints. CoRR abs/2006.05026 (2020) - [i152]Hyun-Suk Lee, Yao Zhang, William R. Zame, Cong Shen, Jang-Won Lee, Mihaela van der Schaar:
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification. CoRR abs/2006.07917 (2020) - [i151]Changhee Lee, Mihaela van der Schaar:
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression. CoRR abs/2006.08600 (2020) - [i150]Ahmed M. Alaa, Mihaela van der Schaar:
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions. CoRR abs/2006.13707 (2020) - [i149]Yao Zhang, William R. Zame, Mihaela van der Schaar:
AutoNCP: Automated pipelines for accurate confidence intervals. CoRR abs/2006.14099 (2020) - [i148]Daniel Jarrett, Mihaela van der Schaar:
Inverse Active Sensing: Modeling and Understanding Timely Decision-Making. CoRR abs/2006.14141 (2020) - [i147]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Strictly Batch Imitation Learning by Energy-based Distribution Matching. CoRR abs/2006.14154 (2020) - [i146]Alex J. Chan, Ahmed M. Alaa, Zhaozhi Qian, Mihaela van der Schaar:
Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift. CoRR abs/2006.14988 (2020) - [i145]Alexis Bellot, Mihaela van der Schaar:
Generalization and Invariances in the Presence of Unobserved Confounding. CoRR abs/2007.10653 (2020) - [i144]James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul W. G. Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, Mihaela van der Schaar:
Hide-and-Seek Privacy Challenge. CoRR abs/2007.12087 (2020) - [i143]Ahmed M. Alaa, Mihaela van der Schaar:
Discriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions. CoRR abs/2007.13481 (2020) - [i142]Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Batch Inverse Reinforcement Learning Using Counterfactuals for Understanding Decision Making. CoRR abs/2007.13531 (2020) - [i141]Zhaozhi Qian, Ahmed M. Alaa, Mihaela van der Schaar:
CPAS: the UK's National Machine Learning-based Hospital Capacity Planning System for COVID-19. CoRR abs/2007.13825 (2020) - [i140]Trent Kyono, Yao Zhang, Mihaela van der Schaar:
CASTLE: Regularization via Auxiliary Causal Graph Discovery. CoRR abs/2009.13180 (2020) - [i139]Jonathan Crabbé, Yao Zhang, William R. Zame, Mihaela van der Schaar:
Learning outside the Black-Box: The pursuit of interpretable models. CoRR abs/2011.08596 (2020) - [i138]James Jordon, Alan Wilson, Mihaela van der Schaar:
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods. CoRR abs/2012.04580 (2020)
2010 – 2019
- 2019
- [j233]David Gesbert, Deniz Gündüz, Paul de Kerret, Chandra R. Murthy, Mihaela van der Schaar, Nicholas D. Sidiropoulos:
Guest Editorial Special Issue on Machine Learning in Wireless Communication - Part I. IEEE J. Sel. Areas Commun. 37(10): 2181-2183 (2019) - [j232]Deniz Gündüz, Paul de Kerret, Nicholas D. Sidiropoulos, David Gesbert, Chandra R. Murthy, Mihaela van der Schaar:
Machine Learning in the Air. IEEE J. Sel. Areas Commun. 37(10): 2184-2199 (2019) - [j231]David Gesbert, Deniz Gündüz, Paul de Kerret, Chandra R. Murthy, Mihaela van der Schaar, Nicholas D. Sidiropoulos:
Guest Editorial Special Issue on Machine Learning in Wireless Communication - Part 2. IEEE J. Sel. Areas Commun. 37(11): 2409-2412 (2019) - [j230]Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar:
Constructing effective personalized policies using counterfactual inference from biased data sets with many features. Mach. Learn. 108(6): 945-970 (2019) - [j229]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Estimating Missing Data in Temporal Data Streams Using Multi-Directional Recurrent Neural Networks. IEEE Trans. Biomed. Eng. 66(5): 1477-1490 (2019) - [j228]Setareh Maghsudi, Mihaela van der Schaar:
Distributed Task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty? IEEE Trans. Cogn. Commun. Netw. 5(1): 165-180 (2019) - [j227]Alexis Bellot, Mihaela van der Schaar:
A Hierarchical Bayesian Model for Personalized Survival Predictions. IEEE J. Biomed. Health Informatics 23(1): 72-80 (2019) - [j226]Yangbo Song, Mihaela van der Schaar:
Repeated Network Games with Dominant Actions and Individual Rationality. IEEE Trans. Netw. Sci. Eng. 6(4): 812-823 (2019) - [j225]Yuanzhang Xiao, Florian Dörfler, Mihaela van der Schaar:
Incentive Design in Peer Review: Rating and Repeated Endogenous Matching. IEEE Trans. Netw. Sci. Eng. 6(4): 898-908 (2019) - [j224]Yiming Zhou, Cong Shen, Mihaela van der Schaar:
A Non-Stationary Online Learning Approach to Mobility Management. IEEE Trans. Wirel. Commun. 18(2): 1434-1446 (2019) - [c268]Kartik Ahuja, William R. Zame, Mihaela van der Schaar:
Optimal Piecewise Approximations for Model Interpretation. ACSSC 2019: 1992-1998 - [c267]Kartik Ahuja, Mihaela van der Schaar:
Joint Concordance Index. ACSSC 2019: 2206-2213 - [c266]Alexis Bellot, Mihaela van der Schaar:
Boosting Transfer Learning with Survival Data from Heterogeneous Domains. AISTATS 2019: 57-65 - [c265]Changhee Lee, William R. Zame, Ahmed M. Alaa, Mihaela van der Schaar:
Temporal Quilting for Survival Analysis. AISTATS 2019: 596-605 - [c264]Onur Atan, William R. Zame, Mihaela van der Schaar:
Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials. AISTATS 2019: 1891-1900 - [c263]Setareh Maghsudi, Mihaela van der Schaar:
A Bandit Learning Approach to Energy-Efficient Femto-Caching under Uncertainty. GLOBECOM 2019: 1-6 - [c262]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks. ICLR 2019 - [c261]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. ICLR (Poster) 2019 - [c260]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
INVASE: Instance-wise Variable Selection using Neural Networks. ICLR (Poster) 2019 - [c259]Ahmed M. Alaa, Mihaela van der Schaar:
Validating Causal Inference Models via Influence Functions. ICML 2019: 191-201 - [c258]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
ASAC: Active Sensing using Actor-Critic models. MLHC 2019: 451-473 - [c257]Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar:
Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis. MLHC 2019: 571-591 - [c256]Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. NeurIPS 2019: 2199-2208 - [c255]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate. NeurIPS 2019: 4325-4334 - [c254]Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar:
Time-series Generative Adversarial Networks. NeurIPS 2019: 5509-5519 - [c253]Ahmed M. Alaa, Mihaela van der Schaar:
Demystifying Black-box Models with Symbolic Metamodels. NeurIPS 2019: 11301-11311 - [c252]Ahmed M. Alaa, Mihaela van der Schaar:
Attentive State-Space Modeling of Disease Progression. NeurIPS 2019: 11334-11344 - [i137]Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar:
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders. CoRR abs/1902.00450 (2019) - [i136]Deniz Gündüz, Paul de Kerret, Nicholas D. Sidiropoulos, David Gesbert, Chandra R. Murthy, Mihaela van der Schaar:
Machine Learning in the Air. CoRR abs/1904.12385 (2019) - [i135]Yao Zhang, James Jordon, Ahmed M. Alaa, Mihaela van der Schaar:
Lifelong Bayesian Optimization. CoRR abs/1905.12280 (2019) - [i134]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
ASAC: Active Sensing using Actor-Critic models. CoRR abs/1906.06796 (2019) - [i133]Alexis Bellot, Mihaela van der Schaar:
Conditional Independence Testing using Generative Adversarial Networks. CoRR abs/1907.04068 (2019) - [i132]Trent Kyono, Mihaela van der Schaar:
Improving Model Robustness Using Causal Knowledge. CoRR abs/1911.12441 (2019) - [i131]Alexis Bellot, Mihaela van der Schaar:
A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records. CoRR abs/1912.09086 (2019) - 2018
- [j223]Fergus Imrie, Anthony R. Bradley, Mihaela van der Schaar, Charlotte M. Deane:
Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data. J. Chem. Inf. Model. 58(11): 2319-2330 (2018) - [j222]Ahmed M. Alaa, Mihaela van der Schaar:
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference. J. Mach. Learn. Res. 19: 4:1-4:62 (2018) - [j221]Cong Shen, Ruida Zhou, Cem Tekin, Mihaela van der Schaar:
Generalized Global Bandit and Its Application in Cellular Coverage Optimization. IEEE J. Sel. Top. Signal Process. 12(1): 218-232 (2018) - [j220]Hyun-Suk Lee, Cem Tekin, Mihaela van der Schaar, Jang-Won Lee:
Adaptive Contextual Learning for Unit Commitment in Microgrids With Renewable Energy Sources. IEEE J. Sel. Top. Signal Process. 12(4): 688-702 (2018) - [j219]Ahmed M. Alaa, Mihaela van der Schaar:
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms. IEEE J. Sel. Top. Signal Process. 12(5): 1031-1046 (2018) - [j218]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Prognosis Using Mixtures of Gaussian Processes. IEEE Trans. Biomed. Eng. 65(1): 207-218 (2018) - [j217]Onur Atan, Cem Tekin, Mihaela van der Schaar:
Global Bandits. IEEE Trans. Neural Networks Learn. Syst. 29(12): 5798-5811 (2018) - [j216]Ahmed M. Alaa, Kartik Ahuja, Mihaela van der Schaar:
A Micro-Foundation of Social Capital in Evolving Social Networks. IEEE Trans. Netw. Sci. Eng. 5(1): 14-31 (2018) - [j215]Randall B. Hellman, Cem Tekin, Mihaela van der Schaar, Veronica J. Santos:
Functional Contour-following via Haptic Perception and Reinforcement Learning. IEEE Trans. Haptics 11(1): 61-72 (2018) - [j214]Sabrina Klos, Cem Tekin, Mihaela van der Schaar, Anja Klein:
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing. IEEE/ACM Trans. Netw. 26(3): 1334-1347 (2018) - [j213]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
ToPs: Ensemble Learning With Trees of Predictors. IEEE Trans. Signal Process. 66(8): 2141-2152 (2018) - [j212]Nima Akbarzadeh, Cem Tekin, Mihaela van der Schaar:
Online Learning in Limit Order Book Trade Execution. IEEE Trans. Signal Process. 66(17): 4626-4641 (2018) - [c251]Onur Atan, James Jordon, Mihaela van der Schaar:
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks. AAAI 2018: 2071-2078 - [c250]Changhee Lee, William R. Zame, Jinsung Yoon, Mihaela van der Schaar:
DeepHit: A Deep Learning Approach to Survival Analysis With Competing Risks. AAAI 2018: 2314-2321 - [c249]Alexis Bellot, Mihaela van der Schaar:
Tree-based Bayesian Mixture Model for Competing Risks. AISTATS 2018: 910-918 - [c248]Setareh Maghsudi, Mihaela van der Schaar:
Distributed Task Management in Cyber-Physical Systems: How to Cooperate Under Uncertainty? GLOBECOM 2018: 206-212 - [c247]Eleonora Giunchiglia, Anton Nemchenko, Mihaela van der Schaar:
RNN-SURV: A Deep Recurrent Model for Survival Analysis. ICANN (3) 2018: 23-32 - [c246]Anton Nemchenko, Trent Kyono, Mihaela van der Schaar:
Siamese Survival Analysis with Competing Risks. ICANN (3) 2018: 260-269 - [c245]Yiming Zhou, Cong Shen, Xiliang Luo, Mihaela van der Schaar:
A Non-Stationary Online Learning Approach to Mobility Management. ICC 2018: 1-6 - [c244]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets. ICLR (Poster) 2018 - [c243]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks. ICLR (Poster) 2018 - [c242]Ahmed M. Alaa, Mihaela van der Schaar:
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design. ICML 2018: 129-138 - [c241]Ahmed M. Alaa, Mihaela van der Schaar:
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning. ICML 2018: 139-148 - [c240]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GAIN: Missing Data Imputation using Generative Adversarial Nets. ICML 2018: 5675-5684 - [c239]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. ICML 2018: 5685-5693 - [c238]Alexis Bellot, Mihaela van der Schaar:
Boosted Trees for Risk Prognosis. MLHC 2018: 2-16 - [c237]Bryan Lim, Mihaela van der Schaar:
Disease-Atlas: Navigating Disease Trajectories using Deep Learning. MLHC 2018: 137-160 - [c236]Alexis Bellot, Mihaela van der Schaar:
Multitask Boosting for Survival Analysis with Competing Risks. NeurIPS 2018: 1397-1406 - [p2]Honglei Li, Yanzhou Liu, Kishan Sudusinghe, Jinsung Yoon, Erik Blasch, Mihaela van der Schaar, Shuvra S. Bhattacharyya:
Design of a Dynamic Data-Driven System for Multispectral Video Processing. Handbook of Dynamic Data Driven Applications Systems 2018: 529-545 - [i130]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks. CoRR abs/1802.06403 (2018) - [i129]Ahmed M. Alaa, Mihaela van der Schaar:
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning. CoRR abs/1802.07207 (2018) - [i128]Onur Atan, William R. Zame, Mihaela van der Schaar:
Learning Optimal Policies from Observational Data. CoRR abs/1802.08679 (2018) - [i127]Bryan Lim, Mihaela van der Schaar:
Disease-Atlas: Navigating Disease Trajectories with Deep Learning. CoRR abs/1803.10254 (2018) - [i126]Jinsung Yoon, James Jordon, Mihaela van der Schaar:
GAIN: Missing Data Imputation using Generative Adversarial Nets. CoRR abs/1806.02920 (2018) - [i125]Kartik Ahuja, William R. Zame, Mihaela van der Schaar:
Piecewise Approximations of Black Box Models for Model Interpretation. CoRR abs/1806.10270 (2018) - [i124]James Jordon, Jinsung Yoon, Mihaela van der Schaar:
Measuring the quality of Synthetic data for use in competitions. CoRR abs/1806.11345 (2018) - [i123]Bryan Lim, Mihaela van der Schaar:
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning. CoRR abs/1807.03159 (2018) - [i122]Anton Nemchenko, Trent Kyono, Mihaela van der Schaar:
Siamese Survival Analysis with Competing Risks. CoRR abs/1807.05935 (2018) - [i121]Onur Atan, William R. Zame, Mihaela van der Schaar:
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation. CoRR abs/1810.02876 (2018) - [i120]Ahmed M. Alaa, Mihaela van der Schaar:
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning. CoRR abs/1810.10489 (2018) - [i119]Kartik Ahuja, Mihaela van der Schaar:
Risk-Stratify: Confident Stratification Of Patients Based On Risk. CoRR abs/1811.00753 (2018) - [i118]Trent Kyono, Fiona J. Gilbert, Mihaela van der Schaar:
MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis. CoRR abs/1811.02661 (2018) - [i117]Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar:
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning. CoRR abs/1811.08943 (2018) - [i116]Carl Rietschel, Jinsung Yoon, Mihaela van der Schaar:
Feature Selection for Survival Analysis with Competing Risks using Deep Learning. CoRR abs/1811.09317 (2018) - [i115]Daniel Jarrett, Jinsung Yoon, Mihaela van der Schaar:
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks. CoRR abs/1811.10746 (2018) - [i114]Owen Lahav, Nicholas Mastronarde, Mihaela van der Schaar:
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems. CoRR abs/1811.10799 (2018) - [i113]Setareh Maghsudi, Mihaela van der Schaar:
Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty? CoRR abs/1812.03557 (2018) - 2017
- [j211]Cem Tekin, Mihaela van der Schaar:
Actionable intelligence and online learning for semantic computing. Encycl. Semantic Comput. Robotic Intell. 1(1): 1630011:1-1630011:8 (2017) - [j210]Simpson Zhang, Mihaela van der Schaar:
From Acquaintances to Friends: Homophily and Learning in Networks. IEEE J. Sel. Areas Commun. 35(3): 680-690 (2017) - [j209]Mihaela van der Schaar, Richard G. Baraniuk, Mung Chiang, Jonathan Huang, Shengdong Zhao:
Introduction to the Issue on Signal Processing and Machine Learning. IEEE J. Sel. Top. Signal Process. 11(5): 713-715 (2017) - [j208]Jie Xu, Kyeong H. Moon, Mihaela van der Schaar:
A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs. IEEE J. Sel. Top. Signal Process. 11(5): 742-753 (2017) - [j207]Jinsung Yoon, Camelia Davtyan, Mihaela van der Schaar:
Discovery and Clinical Decision Support for Personalized Healthcare. IEEE J. Biomed. Health Informatics 21(4): 1133-1145 (2017) - [j206]Chuchu Wu, Mario Gerla, Mihaela van der Schaar:
Social Norm Incentives for Network Coding in Manets. IEEE/ACM Trans. Netw. 25(3): 1761-1774 (2017) - [j205]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning With Confidence Bounds. IEEE Trans. Signal Process. 65(4): 888-903 (2017) - [j204]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Distributed Learning for Stochastic Generalized Nash Equilibrium Problems. IEEE Trans. Signal Process. 65(15): 3893-3908 (2017) - [j203]Sabrina Müller, Onur Atan, Mihaela van der Schaar, Anja Klein:
Context-Aware Proactive Content Caching With Service Differentiation in Wireless Networks. IEEE Trans. Wirel. Commun. 16(2): 1024-1036 (2017) - [c235]Jie Xu, Yuli Han, Daniel Marcu, Mihaela van der Schaar:
Progressive Prediction of Student Performance in College Programs. AAAI 2017: 1604-1610 - [c234]Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar:
Personalized Donor-Recipient Matching for Organ Transplantation. AAAI 2017: 1647-1654 - [c233]Nima Akbarzadeh, Cem Tekin, Mihaela van der Schaar:
Online learning in limit order book trade execution. GlobalSIP 2017: 898-902 - [c232]SaiDhiraj Amuru, R. Michael Buehrer, Mihaela van der Schaar:
Bandit strategies for blindly attacking networks. ICC 2017: 1-6 - [c231]Zhiyang Wang, Cong Shen, Xiliang Luo, Mihaela van der Schaar:
Learn to adapt: Self-optimizing small cell transmit power with correlated bandit learning. ICC 2017: 1-6 - [c230]Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar:
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis. ICML 2017: 60-69 - [c229]Kartik Ahuja, William R. Zame, Mihaela van der Schaar:
DPSCREEN: Dynamic Personalized Screening. NIPS 2017: 1321-1332 - [c228]Cong Shen, Mihaela van der Schaar:
A Learning Approach to Frequent Handover Mitigations in 3GPP Mobility Protocols. WCNC 2017: 1-6 - [i112]Ahmed M. Alaa, Mihaela van der Schaar:
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes. CoRR abs/1704.02801 (2017) - [i111]Jinsung Yoon, William R. Zame, Amitava Banerjee, Martin Cadeiras, Ahmed M. Alaa, Mihaela van der Schaar:
Personalized Survival Predictions for Cardiac Transplantation via Trees of Predictors. CoRR abs/1704.03458 (2017) - [i110]Sabrina Müller, Cem Tekin, Mihaela van der Schaar, Anja Klein:
Context-Aware Hierarchical Online Learning for Performance Maximization in Mobile Crowdsourcing. CoRR abs/1705.03822 (2017) - [i109]Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar:
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis. CoRR abs/1705.05267 (2017) - [i108]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Individualized Risk Prognosis for Critical Care Patients: A Multi-task Gaussian Process Model. CoRR abs/1705.07674 (2017) - [i107]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
ToPs: Ensemble Learning with Trees of Predictors. CoRR abs/1706.01396 (2017) - [i106]Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar:
Deep Counterfactual Networks with Propensity-Dropout. CoRR abs/1706.05966 (2017) - [i105]Jinsung Yoon, William R. Zame, Mihaela van der Schaar:
Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks. CoRR abs/1711.08742 (2017) - [i104]Ahmed M. Alaa, Mihaela van der Schaar:
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms. CoRR abs/1712.08914 (2017) - 2016
- [j202]Zhenlong Yuan, Jie Xu, Yibo Xue, Mihaela van der Schaar:
Bits Learning: User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification. IEEE Commun. Lett. 20(4): 704-707 (2016) - [j201]Cong Shen, Cem Tekin, Mihaela van der Schaar:
A Non-Stochastic Learning Approach to Energy Efficient Mobility Management. IEEE J. Sel. Areas Commun. 34(12): 3854-3868 (2016) - [j200]Jie Xu, Linqi Song, James Y. Xu, Gregory J. Pottie, Mihaela van der Schaar:
Personalized Active Learning for Activity Classification Using Wireless Wearable Sensors. IEEE J. Sel. Top. Signal Process. 10(5): 865-876 (2016) - [j199]Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar:
Context-based unsupervised ensemble learning and feature ranking. Mach. Learn. 105(3): 459-485 (2016) - [j198]Karim Kanoun, Cem Tekin, David Atienza, Mihaela van der Schaar:
Big-Data Streaming Applications Scheduling Based on Staged Multi-Armed Bandits. IEEE Trans. Computers 65(12): 3591-3605 (2016) - [j197]Linqi Song, William Hsu, Jie Xu, Mihaela van der Schaar:
Using Contextual Learning to Improve Diagnostic Accuracy: Application in Breast Cancer Screening. IEEE J. Biomed. Health Informatics 20(3): 902-914 (2016) - [j196]Nicholas Mastronarde, Viral Patel, Jie Xu, Lingjia Liu, Mihaela van der Schaar:
To Relay or Not to Relay: Learning Device-to-Device Relaying Strategies in Cellular Networks. IEEE Trans. Mob. Comput. 15(6): 1569-1585 (2016) - [j195]Ahmed M. Alaa, Kyeong H. Moon, William Hsu, Mihaela van der Schaar:
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening. IEEE Trans. Multim. 18(10): 1942-1955 (2016) - [j194]Suoheng Li, Jie Xu, Mihaela van der Schaar, Weiping Li:
Trend-Aware Video Caching Through Online Learning. IEEE Trans. Multim. 18(12): 2503-2516 (2016) - [j193]Linqi Song, Cem Tekin, Mihaela van der Schaar:
Online Learning in Large-Scale Contextual Recommender Systems. IEEE Trans. Serv. Comput. 9(3): 433-445 (2016) - [j192]Byung-Gook Kim, Yu Zhang, Mihaela van der Schaar, Jang-Won Lee:
Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning. IEEE Trans. Smart Grid 7(5): 2187-2198 (2016) - [j191]Luca Canzian, Ugur Demiryurek, Mihaela van der Schaar:
Collision Detection by Networked Sensors. IEEE Trans. Signal Inf. Process. over Networks 2(1): 1-15 (2016) - [j190]Yannick Meier, Jie Xu, Onur Atan, Mihaela van der Schaar:
Predicting Grades. IEEE Trans. Signal Process. 64(4): 959-972 (2016) - [j189]Jie Xu, Tianwei Xing, Mihaela van der Schaar:
Personalized Course Sequence Recommendations. IEEE Trans. Signal Process. 64(20): 5340-5352 (2016) - [j188]SaiDhiraj Amuru, Cem Tekin, Mihaela van der Schaar, R. Michael Buehrer:
Jamming Bandits - A Novel Learning Method for Optimal Jamming. IEEE Trans. Wirel. Commun. 15(4): 2792-2808 (2016) - [c227]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning with Confidence Bounds for Personalized Diagnosis. AAAI Workshop: Expanding the Boundaries of Health Informatics Using AI 2016 - [c226]Fei Wang, Gregor Stiglic, Mihaela van der Schaar, David A. Sontag, Christopher C. Yang:
Data Mining for Medical Informatics (DMMI) - Learning Health. AMIA 2016 - [c225]Hyun-Suk Lee, Cem Tekin, Mihaela van der Schaar, Jang-Won Lee:
Contextual learning for unit commitment with renewable energy sources. GlobalSIP 2016: 866-870 - [c224]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Adaptive learning for stochastic generalized Nash equilibrium problems. ICASSP 2016: 4840-4844 - [c223]Sabrina Müller, Onur Atan, Mihaela van der Schaar, Anja Klein:
Smart caching in wireless small cell networks via contextual multi-armed bandits. ICC 2016: 1-7 - [c222]William Hoiles, Mihaela van der Schaar:
Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design. ICML 2016: 1596-1604 - [c221]Jinsung Yoon, Ahmed M. Alaa, Scott Hu, Mihaela van der Schaar:
ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission. ICML 2016: 1680-1689 - [c220]Suoheng Li, Jie Xu, Mihaela van der Schaar, Weiping Li:
Popularity-driven content caching. INFOCOM 2016: 1-9 - [c219]William Hoiles, Mihaela van der Schaar:
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics. NIPS 2016: 2020-2028 - [c218]Ahmed M. Alaa, Mihaela van der Schaar:
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition. NIPS 2016: 2910-2918 - [i103]Ahmed M. Alaa, Kyeong H. Moon, William Hsu, Mihaela van der Schaar:
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening. CoRR abs/1602.00374 (2016) - [i102]Kartik Ahuja, Mihaela van der Schaar:
Repeated Matching Mechanism Design with Moral Hazard and Adverse Selection. CoRR abs/1602.02439 (2016) - [i101]Onur Atan, William Whoiles, Mihaela van der Schaar:
Data-Driven Online Decision Making with Costly Information Acquisition. CoRR abs/1602.03600 (2016) - [i100]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Patients using Mixtures of Gaussian Process Experts. CoRR abs/1605.00959 (2016) - [i99]Sabrina Müller, Onur Atan, Mihaela van der Schaar, Anja Klein:
Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks. CoRR abs/1606.04236 (2016) - [i98]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Distributed Learning for Stochastic Generalized Nash Equilibrium Problems. CoRR abs/1608.00039 (2016) - [i97]Cong Shen, Cem Tekin, Mihaela van der Schaar:
A Non-stochastic Learning Approach to Energy Efficient Mobility Management. CoRR abs/1608.07891 (2016) - [i96]Ahmed M. Alaa, Mihaela van der Schaar:
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition. CoRR abs/1610.07505 (2016) - [i95]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
Personalized Risk Scoring for Critical Care Prognosis using Mixtures of Gaussian Processes. CoRR abs/1610.08853 (2016) - [i94]Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar:
Personalized Donor-Recipient Matching for Organ Transplantation. CoRR abs/1611.03934 (2016) - [i93]Ahmed M. Alaa, Jinsung Yoon, Scott Hu, Mihaela van der Schaar:
A Semi-Markov Switching Linear Gaussian Model for Censored Physiological Data. CoRR abs/1611.05146 (2016) - [i92]Ahmed M. Alaa, Mihaela van der Schaar:
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference. CoRR abs/1612.06007 (2016) - [i91]Onur Atan, William R. Zame, Qiaojun Feng, Mihaela van der Schaar:
Constructing Effective Personalized Policies Using Counterfactual Inference from Biased Data Sets with Many Features. CoRR abs/1612.08082 (2016) - 2015
- [j187]Marilyn Wolf, Mihaela van der Schaar, Honggab Kim, Jie Xu:
Caring Analytics for Adults With Special Needs. IEEE Des. Test 32(5): 35-44 (2015) - [j186]Jie Xu, Mihaela van der Schaar:
Incentive-compatible demand-side management for smart grids based on review strategies. EURASIP J. Adv. Signal Process. 2015: 51 (2015) - [j185]Jie Xu, Mihaela van der Schaar:
Efficient Working and Shirking in Information Sharing Networks. IEEE J. Sel. Areas Commun. 33(4): 651-662 (2015) - [j184]Cong Shen, Jie Xu, Mihaela van der Schaar:
Silence is Gold: Strategic Interference Mitigation Using Tokens in Heterogeneous Small Cell Networks. IEEE J. Sel. Areas Commun. 33(6): 1097-1111 (2015) - [j183]Kartik Ahuja, Yuanzhang Xiao, Mihaela van der Schaar:
Distributed Interference Management Policies for Heterogeneous Small Cell Networks. IEEE J. Sel. Areas Commun. 33(6): 1112-1126 (2015) - [j182]Yuanzhang Xiao, Mihaela van der Schaar:
Optimal Foresighted Multi-User Wireless Video. IEEE J. Sel. Top. Signal Process. 9(1): 89-101 (2015) - [j181]Onur Atan, Yiannis Andreopoulos, Cem Tekin, Mihaela van der Schaar:
Bandit Framework for Systematic Learning in Wireless Video-Based Face Recognition. IEEE J. Sel. Top. Signal Process. 9(1): 180-194 (2015) - [j180]Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li:
Forecasting Popularity of Videos Using Social Media. IEEE J. Sel. Top. Signal Process. 9(2): 330-343 (2015) - [j179]Jie Xu, Dingxiong Deng, Ugur Demiryurek, Cyrus Shahabi, Mihaela van der Schaar:
Mining the Situation: Spatiotemporal Traffic Prediction With Big Data. IEEE J. Sel. Top. Signal Process. 9(4): 702-715 (2015) - [j178]Cem Tekin, Mihaela van der Schaar:
RELEAF: An Algorithm for Learning and Exploiting Relevance. IEEE J. Sel. Top. Signal Process. 9(4): 716-727 (2015) - [j177]Luca Canzian, Mihaela van der Schaar:
Real-time stream mining: online knowledge extraction using classifier networks. IEEE Netw. 29(5): 10-16 (2015) - [j176]Kartik Ahuja, Simpson Zhang, Mihaela van der Schaar:
The Population Dynamics of Websites: [Extended Abstract]. SIGMETRICS Perform. Evaluation Rev. 43(3): 53-56 (2015) - [j175]Ahmed M. Alaa, Kartik Ahuja, Mihaela van der Schaar:
Self-Organizing Networks of Information Gathering Cognitive Agents. IEEE Trans. Cogn. Commun. Netw. 1(1): 100-112 (2015) - [j174]SaiDhiraj Amuru, R. Michael Buehrer, Mihaela van der Schaar:
Blind Network Interdiction Strategies - A Learning Approach. IEEE Trans. Cogn. Commun. Netw. 1(4): 435-449 (2015) - [j173]Luca Canzian, Michele Zorzi, Mihaela van der Schaar:
Game Theoretic Design of MAC Protocols: Pricing Versus Intervention. IEEE Trans. Commun. 63(11): 4287-4303 (2015) - [j172]Yuanzhang Xiao, Mihaela van der Schaar:
Socially-Optimal Design of Service Exchange Platforms with Imperfect Monitoring. ACM Trans. Economics and Comput. 3(4): 25:1-25:25 (2015) - [j171]Cem Tekin, Onur Atan, Mihaela van der Schaar:
Discover the Expert: Context-Adaptive Expert Selection for Medical Diagnosis. IEEE Trans. Emerg. Top. Comput. 3(2): 220-234 (2015) - [j170]Cem Tekin, Mihaela van der Schaar:
Active Learning in Context-Driven Stream Mining With an Application to Image Mining. IEEE Trans. Image Process. 24(11): 3666-3679 (2015) - [j169]Luca Canzian, Kun Zhao, Gerard C. L. Wong, Mihaela van der Schaar:
A Dynamic Network Formation Model for Understanding Bacterial Self-Organization Into Micro-Colonies. IEEE Trans. Mol. Biol. Multi Scale Commun. 1(1): 76-89 (2015) - [j168]Cem Tekin, Mihaela van der Schaar:
Contextual Online Learning for Multimedia Content Aggregation. IEEE Trans. Multim. 17(4): 549-561 (2015) - [j167]Nikolaos Thomos, Eymen Kurdoglu, Pascal Frossard, Mihaela van der Schaar:
Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery From Multiple Servers. IEEE Trans. Multim. 17(6): 893-906 (2015) - [j166]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Information-Sharing Over Adaptive Networks With Self-Interested Agents. IEEE Trans. Signal Inf. Process. over Networks 1(1): 2-19 (2015) - [j165]Luca Canzian, Yu Zhang, Mihaela van der Schaar:
Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams. IEEE Trans. Signal Inf. Process. over Networks 1(3): 180-194 (2015) - [j164]Luca Canzian, Mihaela van der Schaar:
Timely Event Detection by Networked Learners. IEEE Trans. Signal Process. 63(5): 1282-1296 (2015) - [j163]Jie Xu, Cem Tekin, Simpson Zhang, Mihaela van der Schaar:
Distributed Multi-Agent Online Learning Based on Global Feedback. IEEE Trans. Signal Process. 63(9): 2225-2238 (2015) - [j162]Cem Tekin, Mihaela van der Schaar:
Distributed Online Learning via Cooperative Contextual Bandits. IEEE Trans. Signal Process. 63(14): 3700-3714 (2015) - [j161]Kartik Ahuja, Yuanzhang Xiao, Mihaela van der Schaar:
Efficient Interference Management Policies for Femtocell Networks. IEEE Trans. Wirel. Commun. 14(9): 4879-4893 (2015) - [c217]Jie Xu, Daby M. Sow, Deepak S. Turaga, Mihaela van der Schaar:
Online Transfer Learning for Differential Diagnosis Determination. AAAI Workshop: WWW and Public Health Intelligence 2015 - [c216]Onur Atan, Cem Tekin, Mihaela van der Schaar:
Global Multi-armed Bandits with Hölder Continuity. AISTATS 2015 - [c215]Karim Kanoun, Mihaela van der Schaar:
Big-data streaming applications scheduling with online learning and concept drift detection. DATE 2015: 1547-1550 - [c214]Yuanzhang Xiao, Mihaela van der Schaar:
Optimal intervention for incentivizing the adoption of commercial electric vehicles. GlobalSIP 2015: 508-512 - [c213]SaiDhiraj Amuru, Yuanzhang Xiao, Mihaela van der Schaar, R. Michael Buehrer:
To Send or Not to Send - Learning MAC Contention. GLOBECOM 2015: 1-6 - [c212]Onur Atan, William Hsu, Cem Tekin, Mihaela van der Schaar:
A data-driven approach for matching clinical expertise to individual cases. ICASSP 2015: 2105-2109 - [c211]Sergio Barbarossa, Paolo Di Lorenzo, Mihaela van der Schaar:
Network formation games based on conditional independence graphs. ICASSP 2015: 2944-2948 - [c210]Cem Tekin, Jonas Braun, Mihaela van der Schaar:
eTutor: Online learning for personalized education. ICASSP 2015: 5545-5549 - [c209]SaiDhiraj Amuru, Cem Tekin, Mihaela van der Schaar, R. Michael Buehrer:
A systematic learning method for optimal jamming. ICC 2015: 2822-2827 - [c208]Kishan Sudusinghe, Yang Jiao, Haifa Ben Salem, Mihaela van der Schaar, Shuvra S. Bhattacharyya:
Multiobjective Design Optimization in the Lightweight Dataflow for DDDAS Environment (LiD4E)1. ICCS 2015: 2563-2572 - [c207]Yannick Meier, Jie Xu, Onur Atan, Mihaela van der Schaar:
Personalized Grade Prediction: A Data Mining Approach. ICDM 2015: 907-912 - [c206]Erfan Soltanmohammadi, Mort Naraghi-Pour, Mihaela van der Schaar:
Context-based Unsupervised Data Fusion for Decision Making. ICML 2015: 2076-2084 - [c205]Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li:
Timely video popularity forecasting based on social networks. INFOCOM 2015: 2308-2316 - [c204]Zhenlong Yuan, Yibo Xue, Mihaela van der Schaar:
BitMiner: Bits Mining in Internet Traffic Classification. SIGCOMM 2015: 93-94 - [c203]Valerio Di Valerio, Chiara Petrioli, Loreto Pescosolido, Mihaela van der Schaar:
A Reinforcement Learning-based Data-Link Protocol for Underwater Acoustic Communications. WUWNet 2015: 2:1-2:5 - [i90]Cem Tekin, Mihaela van der Schaar:
RELEAF: An Algorithm for Learning and Exploiting Relevance. CoRR abs/1502.01418 (2015) - [i89]Cem Tekin, Mihaela van der Schaar:
Contextual Online Learning for Multimedia Content Aggregation. CoRR abs/1502.02125 (2015) - [i88]Ahmed M. Alaa, Kartik Ahuja, Mihaela van der Schaar:
Self-organizing Networks of Information Gathering Cognitive Agents. CoRR abs/1503.04768 (2015) - [i87]Onur Atan, Cem Tekin, Mihaela van der Schaar:
Global Bandits. CoRR abs/1503.08370 (2015) - [i86]Kartik Ahuja, Yuanzhang Xiao, Mihaela van der Schaar:
Efficient Interference Management Policies for Femtocell Networks. CoRR abs/1504.07009 (2015) - [i85]Simpson Zhang, Mihaela van der Schaar:
Reputational Learning and Network Dynamics. CoRR abs/1507.04065 (2015) - [i84]Ahmed M. Alaa, Kartik Ahuja, Mihaela van der Schaar:
Evolution of Social Networks: A Microfounded Model. CoRR abs/1508.00205 (2015) - [i83]Cem Tekin, Mihaela van der Schaar:
Staged Multi-armed Bandits. CoRR abs/1508.00641 (2015) - [i82]Yannick Meier, Jie Xu, Onur Atan, Mihaela van der Schaar:
Predicting Grades. CoRR abs/1508.03865 (2015) - [i81]Yangbo Song, Mihaela van der Schaar:
Dynamic Network Formation with Foresighted Agents. CoRR abs/1509.00126 (2015) - [i80]Mihaela van der Schaar, Simpson Zhang:
From Acquaintances to Friends: Homophily and Learning in Networks. CoRR abs/1510.08103 (2015) - [i79]Ahmed M. Alaa, Kartik Ahuja, Mihaela van der Schaar:
A Micro-foundation of Social Capital in Evolving Social Networks. CoRR abs/1511.02429 (2015) - [i78]Cem Tekin, Jinsung Yoon, Mihaela van der Schaar:
Adaptive Ensemble Learning with Confidence Bounds. CoRR abs/1512.07446 (2015) - [i77]Jie Xu, Tianwei Xing, Mihaela van der Schaar:
Personalized Course Sequence Recommendations. CoRR abs/1512.09176 (2015) - 2014
- [j160]William R. Zame, Jie Xu, Mihaela van der Schaar:
Cooperative Multi-Agent Learning and Coordination for Cognitive Radio Networks. IEEE J. Sel. Areas Commun. 32(3): 464-477 (2014) - [j159]Jie Xu, Yiannis Andreopoulos, Yuanzhang Xiao, Mihaela van der Schaar:
Non-Stationary Resource Allocation Policies for Delay-Constrained Video Streaming: Application to Video over Internet-of-Things-Enabled Networks. IEEE J. Sel. Areas Commun. 32(4): 782-794 (2014) - [j158]Linqi Song, Yuanzhang Xiao, Mihaela van der Schaar:
Demand Side Management in Smart Grids Using a Repeated Game Framework. IEEE J. Sel. Areas Commun. 32(7): 1412-1424 (2014) - [j157]Cem Tekin, Simpson Zhang, Mihaela van der Schaar:
Distributed Online Learning in Social Recommender Systems. IEEE J. Sel. Top. Signal Process. 8(4): 638-652 (2014) - [j156]Jie Xu, Yangbo Song, Mihaela van der Schaar:
Sharing in Networks of Strategic Agents. IEEE J. Sel. Top. Signal Process. 8(4): 717-731 (2014) - [j155]Yu Zhang, Mihaela van der Schaar:
Structure-Aware Stochastic Storage Management in Smart Grids. IEEE J. Sel. Top. Signal Process. 8(6): 1098-1110 (2014) - [j154]Mahnoosh Alizadeh, Yuanzhang Xiao, Anna Scaglione, Mihaela van der Schaar:
Dynamic Incentive Design for Participation in Direct Load Scheduling Programs. IEEE J. Sel. Top. Signal Process. 8(6): 1111-1126 (2014) - [j153]Jie Xu, Mihaela van der Schaar:
Incentive design for heterogeneous user-generated content networks. SIGMETRICS Perform. Evaluation Rev. 41(4): 34-37 (2014) - [j152]Yu Zhang, Daby M. Sow, Deepak S. Turaga, Mihaela van der Schaar:
A fast online learning algorithm for distributed mining of BigData. SIGMETRICS Perform. Evaluation Rev. 41(4): 90-93 (2014) - [j151]Karim Kanoun, Nicholas Mastronarde, David Atienza, Mihaela van der Schaar:
Online Energy-Efficient Task-Graph Scheduling for Multicore Platforms. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 33(8): 1194-1207 (2014) - [j150]Yuanzhang Xiao, Mihaela van der Schaar:
Energy-Efficient Nonstationary Spectrum Sharing. IEEE Trans. Commun. 62(3): 810-821 (2014) - [j149]Yu Zhang, Jaeok Park, Mihaela van der Schaar:
Rating Protocols in Online Communities. ACM Trans. Economics and Comput. 2(1): 4:1-4:34 (2014) - [j148]Shaolei Ren, Mihaela van der Schaar:
Dynamic Scheduling and Pricing in Wireless Cloud Computing. IEEE Trans. Mob. Comput. 13(10): 2283-2292 (2014) - [j147]Yu Zhang, Mihaela van der Schaar:
Collective Ratings for Online Communities With Strategic Users. IEEE Trans. Signal Process. 62(12): 3069-3083 (2014) - [j146]Shaolei Ren, Nikos Deligiannis, Yiannis Andreopoulos, Mohammad A. Islam, Mihaela van der Schaar:
Dynamic Scheduling for Energy Minimization in Delay-Sensitive Stream Mining. IEEE Trans. Signal Process. 62(20): 5439-5448 (2014) - [j145]Saeedeh Parsaeefard, Ahmad R. Sharafat, Mihaela van der Schaar:
Robust Additively Coupled Games in the Presence of Bounded Uncertainty in Communication Networks. IEEE Trans. Veh. Technol. 63(3): 1436-1452 (2014) - [j144]Saeedeh Parsaeefard, Mihaela van der Schaar, Ahmad R. Sharafat:
Robust Power Control for Heterogeneous Users in Shared Unlicensed Bands. IEEE Trans. Wirel. Commun. 13(6): 3167-3182 (2014) - [j143]Juan J. Alcaraz, Mihaela van der Schaar:
Coalitional Games With Intervention: Application to Spectrum Leasing in Cognitive Radio. IEEE Trans. Wirel. Commun. 13(11): 6166-6179 (2014) - [j142]Yuanzhang Xiao, William R. Zame, Mihaela van der Schaar:
Technology Choices and Pricing Policies in Public and Private Wireless Networks. IEEE Trans. Wirel. Commun. 13(12): 6606-6618 (2014) - [c202]Yuanzhang Xiao, Florian Dörfler, Mihaela van der Schaar:
Rating and matching in peer review systems. Allerton 2014: 54-61 - [c201]Cem Tekin, Luca Canzian, Mihaela van der Schaar:
Context-adaptive big data stream mining. Allerton 2014: 483-490 - [c200]Cem Tekin, Mihaela van der Schaar:
An experts learning approach to mobile service offloading. Allerton 2014: 643-650 - [c199]Jie Xu, Simpson Zhang, Mihaela van der Schaar:
Network evolution with incomplete information and learning. Allerton 2014: 1163-1168 - [c198]Karim Kanoun, David Atienza, Nicholas Mastronarde, Mihaela van der Schaar:
A unified online directed acyclic graph flow manager for multicore schedulers. ASP-DAC 2014: 714-719 - [c197]Dimitrios Katselis, Carolyn L. Beck, Mihaela van der Schaar:
Ensemble Online Clustering through Decentralized Observations. CDC 2014: 910-915 - [c196]Yuanzhang Xiao, Mihaela van der Schaar:
Decentralized foresighted energy purchase and procurement with renewable generation and energy storage. CDC 2014: 5630-5635 - [c195]Kartik Ahuja, Simpson Zhang, Mihaela van der Schaar:
Towards a theory of societal co-evolution: Individualism versus collectivism. GlobalSIP 2014: 769-773 - [c194]Yuanzhang Xiao, Kartik Ahuja, Mihaela van der Schaar:
Spectrum sharing for delay-sensitive applications with continuing QoS guarantees. GLOBECOM 2014: 1265-1270 - [c193]Jie Xu, James Y. Xu, Linqi Song, Gregory J. Pottie, Mihaela van der Schaar:
Context-driven online learning for activity classification in wireless health. GLOBECOM 2014: 2423-2428 - [c192]Cong Shen, Jie Xu, Mihaela van der Schaar:
Silence is gold: Strategic small cell interference management using tokens. GLOBECOM 2014: 4359-4365 - [c191]Onur Atan, Cem Tekin, Mihaela van der Schaar, Yiannis Andreopoulos:
Bandit framework for systematic learning in wireless video-based face recognition. ICASSP 2014: 704-708 - [c190]Yuanzhang Xiao, Mihaela van der Schaar:
Optimal foresighted packet scheduling and resource allocation for multi-user video transmission in 4G cellular networks. ICASSP 2014: 709-713 - [c189]Luca Canzian, Mihaela van der Schaar:
A network of cooperative learners for data-driven stream Mining. ICASSP 2014: 2908-2912 - [c188]Linqi Song, Cem Tekin, Mihaela van der Schaar:
Clustering based online learning in recommender systems: A bandit approach. ICASSP 2014: 4528-4532 - [c187]Jie Xu, Yangbo Song, Mihaela van der Schaar:
Incentivizing information sharing in networks. ICASSP 2014: 5467-5471 - [c186]Juan J. Alcaraz, Mihaela van der Schaar:
Intervention framework for counteracting collusion in spectrum leasing systems. ICASSP 2014: 7318-7322 - [c185]Linqi Song, Yuanzhang Xiao, Mihaela van der Schaar:
Non-stationary demand side management method for smart grids. ICASSP 2014: 7759-7763 - [c184]Kishan Sudusinghe, Inkeun Cho, Mihaela van der Schaar, Shuvra S. Bhattacharyya:
Model Based Design Environment for Data-driven Embedded Signal Processing Systems. ICCS 2014: 1193-1202 - [c183]Mihaela van der Schaar:
Real-time discovery and decision making from big data. ICCE-TW 2014: 1-3 - [c182]Jie Xu, Dingxiong Deng, Ugur Demiryurek, Cyrus Shahabi, Mihaela van der Schaar:
Context-Aware Online Spatiotemporal Traffic Prediction. ICDM Workshops 2014: 43-46 - [c181]Byung-Gook Kim, Yu Zhang, Mihaela van der Schaar, Jang-Won Lee:
Dynamic pricing for smart grid with reinforcement learning. INFOCOM Workshops 2014: 640-645 - [c180]Yu Zhang, Mihaela van der Schaar:
Structure-aware stochastic load management in smart grids. INFOCOM 2014: 2643-2651 - [c179]Karim Kanoun, Martino Ruggiero, David Atienza, Mihaela van der Schaar:
Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing. ISVLSI 2014: 468-473 - [c178]Cem Tekin, Mihaela van der Schaar:
Discovering, Learning and Exploiting Relevance. NIPS 2014: 1233-1241 - [c177]Mihaela van der Schaar, Simpson Zhang:
A dynamic model of certification and reputation. EC 2014: 967-968 - [i76]Jie Xu, Yiannis Andreopoulos, Yuanzhang Xiao, Mihaela van der Schaar:
Non-stationary Resource Allocation Policies for Delay-constrained Video Streaming: Application to Video over Internet-of-Things-enabled Networks. CoRR abs/1401.0855 (2014) - [i75]Yuanzhang Xiao, Mihaela van der Schaar:
Foresighted Demand Side Management. CoRR abs/1401.2185 (2014) - [i74]Jie Xu, Mihaela van der Schaar, Jiangchuan Liu, Haitao Li:
Forecasting Popularity of Videos using Social Media. CoRR abs/1403.5603 (2014) - [i73]Nikolaos Thomos, Eymen Kurdoglu, Pascal Frossard, Mihaela van der Schaar:
Adaptive Prioritized Random Linear Coding and Scheduling for Layered Data Delivery from Multiple Servers. CoRR abs/1409.8650 (2014) - [i72]Cem Tekin, Mihaela van der Schaar:
eTutor: Online Learning for Personalized Education. CoRR abs/1410.3617 (2014) - [i71]Onur Atan, Cem Tekin, Mihaela van der Schaar:
Global Bandits with Holder Continuity. CoRR abs/1410.7890 (2014) - [i70]Luca Canzian, Kun Zhao, Gerard C. L. Wong, Mihaela van der Schaar:
A Dynamic Network Formation Model for Understanding Bacterial Self-Organization into Micro-Colonies. CoRR abs/1410.8091 (2014) - [i69]Yuanzhang Xiao, Florian Dörfler, Mihaela van der Schaar:
Incentive Design in Peer Review: Rating and Repeated Endogenous Matching. CoRR abs/1411.2139 (2014) - [i68]SaiDhiraj Amuru, Cem Tekin, Mihaela van der Schaar, R. Michael Buehrer:
Jamming Bandits. CoRR abs/1411.3652 (2014) - [i67]Kartik Ahuja, Yuanzhang Xiao, Mihaela van der Schaar:
Distributed Interference Management Policies for Heterogeneous Small Cell Networks. CoRR abs/1411.5102 (2014) - [i66]Kartik Ahuja, Simpson Zhang, Mihaela van der Schaar:
Towards a Theory of Societal Co-Evolution: Individualism versus Collectivism. CoRR abs/1411.5107 (2014) - [i65]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Information-Sharing over Adaptive Networks with Self-interested Agents. CoRR abs/1412.1468 (2014) - 2013
- [j141]Yu Zhang, Mihaela van der Schaar:
Strategic Networks: Information Dissemination and Link Formation Among Self-Interested Agents. IEEE J. Sel. Areas Commun. 31(6): 1115-1123 (2013) - [j140]Byung-Gook Kim, Shaolei Ren, Mihaela van der Schaar, Jang-Won Lee:
Bidirectional Energy Trading and Residential Load Scheduling with Electric Vehicles in the Smart Grid. IEEE J. Sel. Areas Commun. 31(7): 1219-1234 (2013) - [j139]Yu Zhang, Mihaela van der Schaar:
Incentive Provision and Job Allocation in Social Cloud Systems. IEEE J. Sel. Areas Commun. 31(9-Supplement): 607-617 (2013) - [j138]William R. Zame, Jie Xu, Mihaela van der Schaar:
Winning the Lottery: Learning Perfect Coordination With Minimal Feedback. IEEE J. Sel. Top. Signal Process. 7(5): 846-857 (2013) - [j137]Yu Zhang, Mihaela van der Schaar:
Robust Reputation Protocol Design for Online Communities: A Stochastic Stability Analysis. IEEE J. Sel. Top. Signal Process. 7(5): 907-920 (2013) - [j136]Jie Xu, Mihaela van der Schaar:
Token System Design for Autonomic Wireless Relay Networks. IEEE Trans. Commun. 61(7): 2924-2935 (2013) - [j135]Luca Canzian, Yuanzhang Xiao, William R. Zame, Michele Zorzi, Mihaela van der Schaar:
Intervention with Private Information, Imperfect Monitoring and Costly Communication. IEEE Trans. Commun. 61(8): 3192-3205 (2013) - [j134]Luca Canzian, Yuanzhang Xiao, William R. Zame, Michele Zorzi, Mihaela van der Schaar:
Intervention with Complete and Incomplete Information: Application to Flow Control. IEEE Trans. Commun. 61(8): 3206-3218 (2013) - [j133]Nicholas Mastronarde, Mihaela van der Schaar:
Joint Physical-Layer and System-Level Power Management for Delay-Sensitive Wireless Communications. IEEE Trans. Mob. Comput. 12(4): 694-709 (2013) - [j132]Nicholas Mastronarde, Karim Kanoun, David Atienza, Pascal Frossard, Mihaela van der Schaar:
Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoders on Multicore Systems. IEEE Trans. Multim. 15(2): 268-278 (2013) - [j131]Shaolei Ren, Mihaela van der Schaar:
Efficient Resource Provisioning and Rate Selection for Stream Mining in a Community Cloud. IEEE Trans. Multim. 15(4): 723-734 (2013) - [j130]Shaolei Ren, Jaeok Park, Mihaela van der Schaar:
Entry and Spectrum Sharing Scheme Selection in Femtocell Communications Markets. IEEE/ACM Trans. Netw. 21(1): 218-232 (2013) - [j129]Oussama Habachi, Hsien-Po Shiang, Mihaela van der Schaar, Yezekael Hayel:
Online Learning Based Congestion Control for Adaptive Multimedia Transmission. IEEE Trans. Signal Process. 61(6): 1460-1469 (2013) - [j128]Hsien-Po Shiang, Mihaela van der Schaar:
Conjecture-Based Load Balancing for Delay-Sensitive Users Without Message Exchanges. IEEE Trans. Veh. Technol. 62(8): 3983-3995 (2013) - [j127]Khoa Tran Phan, Tho Le-Ngoc, Mihaela van der Schaar, Fangwen Fu:
Optimal Scheduling over Time-Varying Channels with Traffic Admission Control: Structural Results and Online Learning Algorithms. IEEE Trans. Wirel. Commun. 12(9): 4434-4444 (2013) - [c176]Yuanzhang Xiao, Mihaela van der Schaar:
Distributed demand side management among foresighted decision makers in power networks. ACSSC 2013: 1383-1387 - [c175]Yuanzhang Xiao, Mihaela van der Schaar:
Spectrum sharing policies for heterogeneous delay-sensitive users: A novel design framework. Allerton 2013: 85-92 - [c174]Yu Zhang, Mihaela van der Schaar:
Strategic information dissemination in endogenous networks. Allerton 2013: 389-396 - [c173]Jie Xu, Cem Tekin, Mihaela van der Schaar:
Learning optimal classifier chains for real-time big data mining. Allerton 2013: 512-519 - [c172]Luca Canzian, Yuanzhang Xiao, Michele Zorzi, Mihaela van der Schaar:
Game theoretic design of MAC protocols: Pricing and intervention in slotted-Aloha. Allerton 2013: 707-714 - [c171]Yuanzhang Xiao, Mihaela van der Schaar:
Nonstationary resource sharing with imperfect binary feedback: An optimal design framework for cost minimization. Allerton 2013: 932-939 - [c170]Mahnoosh Alizadeh, Yuanzhang Xiao, Anna Scaglione, Mihaela van der Schaar:
Incentive design for Direct Load Control programs. Allerton 2013: 1029-1036 - [c169]Cem Tekin, Mihaela van der Schaar:
Distributed online Big Data classification using context information. Allerton 2013: 1435-1442 - [c168]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Distributed spectrum sensing in the presence of selfish users. CAMSAP 2013: 392-395 - [c167]Chung-Kai Yu, Mihaela van der Schaar, Ali H. Sayed:
Cluster formation over adaptive networks with selfish agents. EUSIPCO 2013: 1-5 - [c166]Nicholas Mastronarde, Viral Patel, Jie Xu, Mihaela van der Schaar:
Learning relaying strategies in cellular D2D networks with token-based incentives. GLOBECOM Workshops 2013: 163-169 - [c165]Eunhye Choi, Suin Song, Hyejin Kim, Jiyeon Hong, Hyunggon Park, Mihaela van der Schaar:
Utility-based server management strategy in cloud networks. GLOBECOM Workshops 2013: 464-469 - [c164]Yuanzhang Xiao, Mihaela van der Schaar:
Energy-efficient nonstationary power control in cognitive radio networks.