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ORCIDMihaela van der Schaar
Mihaela van der Schaar-Mitrea
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- affiliation: University of Cambridge, epartment of Applied Mathematics and Theoretical Physics, UK
- affiliation: University of California, Los Angeles, Electrical Engineering Department
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2020 – today
- 2023
[j255]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)- [i229]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) - [i228]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) - [i227]Evgeny S. Saveliev, Mihaela van der Schaar:
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine. CoRR abs/2301.12260 (2023) - [i226]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) - [i225]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. CoRR abs/2302.12238 (2023) - [i224]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) - [i223]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) - [i222]Yuchao Qin, Mihaela van der Schaar, Changhee Lee:
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression. CoRR abs/2302.12619 (2023) - [i221]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) - [i220]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) - [i219]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) - [i218]Jeroen Berrevoets, Krzysztof Kacprzyk, Zhaozhi Qian, Mihaela van der Schaar:
Causal Deep Learning. CoRR abs/2303.02186 (2023) - [i217]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) - [i216]Eleonora Giunchiglia, Fergus Imrie, Mihaela van der Schaar, Thomas Lukasiewicz:
Machine Learning with Requirements: a Manifesto. CoRR abs/2304.03674 (2023) - [i215]Boris van Breugel, Mihaela van der Schaar:
Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data. CoRR abs/2304.03722 (2023) - [i214]Jonathan Crabbé, Mihaela van der Schaar:
Evaluating the Robustness of Interpretability Methods through Explanation Invariance and Equivariance. CoRR abs/2304.06715 (2023) - [i213]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) - 2022
- [j254]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) - [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) - [c344]Alihan Hüyük, William R. Zame, Mihaela van der Schaar:
Inferring Lexicographically-Ordered Rewards from Preferences. AAAI 2022: 5737-5745 - [c343]Yao Zhang, Jeroen Berrevoets, Mihaela van der Schaar:
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects. AISTATS 2022: 4158-4177 - [c342]Alexis Bellot, Kim Branson, Mihaela van der Schaar:
Neural graphical modelling in continuous-time: consistency guarantees and algorithms. ICLR 2022 - [c341]Alex J. Chan, Alicia Curth, Mihaela van der Schaar:
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies. ICLR 2022 - [c340]Changhee Lee, Fergus Imrie, Mihaela van der Schaar:
Self-Supervision Enhanced Feature Selection with Correlated Gates. ICLR 2022 - [c339]Alizée Pace, Alex J. Chan, Mihaela van der Schaar:
POETREE: Interpretable Policy Learning with Adaptive Decision Trees. ICLR 2022 - [c338]Zhaozhi Qian, Krzysztof Kacprzyk, Mihaela van der Schaar:
D-CODE: Discovering Closed-form ODEs from Observed Trajectories. ICLR 2022 - [c337]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 - [c336]Jonathan Crabbé, Mihaela van der Schaar:
Label-Free Explainability for Unsupervised Models. ICML 2022: 4391-4420 - [c335]Samuel Holt, Zhaozhi Qian, Mihaela van der Schaar:
Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. ICML 2022: 8811-8832 - [c334]Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar:
Inverse Contextual Bandits: Learning How Behavior Evolves over Time. ICML 2022: 9506-9524 - [c333]Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, Mihaela van der Schaar:
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection. ICML 2022: 9916-9937 - [c332]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. ICML 2022: 19467-19496 - [c331]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 - [c330]Mihaela van der Schaar:
Machine Learning for Medicine and Healthcare. ICPRAM 2022: 5 - [c329]Ioana Bica, Mihaela van der Schaar:
Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation. NeurIPS 2022 - [c328]Alex J. Chan, Mihaela van der Schaar:
Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning. NeurIPS 2022 - [c327]Jonathan Crabbé, Alicia Curth, Ioana Bica, Mihaela van der Schaar:
Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability. NeurIPS 2022 - [c326]Jonathan Crabbé, Mihaela van der Schaar:
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations. NeurIPS 2022 - [c325]Fergus Imrie, Alexander Norcliffe, Pietro Lió, Mihaela van der Schaar:
Composite Feature Selection Using Deep Ensembles. NeurIPS 2022 - [c324]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Online Decision Mediation. NeurIPS 2022 - [c323]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) - [c322]Changhee Lee, Mihaela van der Schaar:
A Variational Information Bottleneck Approach to Multi-Omics Data Integration. AISTATS 2021: 1513-1521 - [c321]Alicia Curth, Mihaela van der Schaar:
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. AISTATS 2021: 1810-1818 - [c320]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 - [c319]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 - [c318]Mihaela van der Schaar:
Building next-generation healthcare systems using distributed machine learning. ICDCN 2021: 4 - [c317]Ahmed M. Alaa, Alex James Chan, Mihaela van der Schaar:
Generative Time-series Modeling with Fourier Flows. ICLR 2021 - [c316]Ioana Bica, Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Learning "What-if" Explanations for Sequential Decision-Making. ICLR 2021 - [c315]Alex James Chan, Mihaela van der Schaar:
Scalable Bayesian Inverse Reinforcement Learning. ICLR 2021 - [c314]Alihan Hüyük, Daniel Jarrett, Cem Tekin, Mihaela van der Schaar:
Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning. ICLR 2021 - [c313]Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, Mihaela van der Schaar:
Clairvoyance: A Pipeline Toolkit for Medical Time Series. ICLR 2021 - [c312]Alexis Bellot, Mihaela van der Schaar:
Policy Analysis using Synthetic Controls in Continuous-Time. ICML 2021: 759-768 - [c311]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 - [c310]Jonathan Crabbé, Mihaela van der Schaar:
Explaining Time Series Predictions with Dynamic Masks. ICML 2021: 2166-2177 - [c309]Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar:
Inverse Decision Modeling: Learning Interpretable Representations of Behavior. ICML 2021: 4755-4771 - [c308]Ioana Bica, Daniel Jarrett, Mihaela van der Schaar:
Invariant Causal Imitation Learning for Generalizable Policies. NeurIPS 2021: 3952-3964 - [c307]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 - [c306]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 - [c305]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 - [c304]Zhaozhi Qian, Yao Zhang, Ioana Bica, Angela M. Wood, Mihaela van der Schaar:
SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes. NeurIPS 2021: 3178-3190 - [c303]Kamile Stankeviciute, Ahmed M. Alaa, Mihaela van der Schaar:
Conformal Time-series Forecasting. NeurIPS 2021: 6216-6228 - [c302]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 - [c301]Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie, Mihaela van der Schaar:
Explaining Latent Representations with a Corpus of Examples. NeurIPS 2021: 12154-12166 - [c300]Alicia Curth, Mihaela van der Schaar:
On Inductive Biases for Heterogeneous Treatment Effect Estimation. NeurIPS 2021: 15883-15894 - [c299]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 - [c298]Zhaozhi Qian, Alicia Curth, Mihaela van der Schaar:
Estimating Multi-cause Treatment Effects via Single-cause Perturbation. NeurIPS 2021: 23754-23767 - [c297]Trent Kyono, Yao Zhang, Alexis Bellot, Mihaela van der Schaar:
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms. NeurIPS 2021: 23806-23817 - [c296]Alicia Curth, Changhee Lee, Mihaela van der Schaar:
SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data. NeurIPS 2021: 26740-26753 - [c295]Daniel Jarrett, Ioana Bica, Mihaela van der Schaar:
Time-series Generation by Contrastive Imitation. NeurIPS 2021: 28968-28982 - [c294]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 - [c293]Alexis Bellot, Mihaela van der Schaar:
Application of kernel hypothesis testing on set-valued data. UAI 2021: 194-204 - [c292]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]