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ORCIDTransactions on Machine Learning Research, Volume 2024
Volume 2024, 2024
- Melissa Hall, Candace Ross, Adina Williams, Nicolas Carion, Michal Drozdzal, Adriana Romero-Soriano:
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity. - Taiga Abe, Estefany Kelly Buchanan, Geoff Pleiss, John P. Cunningham:
Pathologies of Predictive Diversity in Deep Ensembles. - Shehzaad Zuzar Dhuliawala, Mrinmaya Sachan, Carl Allen:
Variational Classification: A Probabilistic Generalization of the Softmax Classifier. - Shihao Liang, Runchu Tian, Kunlun Zhu, Yujia Qin, Huadong Wang, Xin Cong, Zhiyuan Liu, Xiaojiang Liu, Maosong Sun:
Exploring Format Consistency for Instruction Tuning. - Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell:
Neural Implicit Manifold Learning for Topology-Aware Density Estimation. - Kazuma Suetake, Takuya Ushimaru, Ryuji Saiin, Yoshihide Sawada:
Synaptic Interaction Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks. - Alexis Teter, Iman Nodozi, Abhishek Halder:
Proximal Mean Field Learning in Shallow Neural Networks. - Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, Yongbin Li:
A Survey on Out-of-Distribution Detection in NLP. - Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang, Mark Coates:
DyG2Vec: Efficient Representation Learning for Dynamic Graphs. - Vincent Abbott:
Neural Circuit Diagrams: Robust Diagrams for the Communication, Implementation, and Analysis of Deep Learning Architectures. - Fang Kong, Xiangcheng Zhang, Baoxiang Wang, Shuai Li:
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization. - Lorenzo Luzi, Paul M. Mayer, Josue Casco-Rodriguez, Ali Siahkoohi, Richard G. Baraniuk:
Boomerang: Local sampling on image manifolds using diffusion models. - Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mido Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jégou, Julien Mairal, Patrick Labatut, Armand Joulin, Piotr Bojanowski:
DINOv2: Learning Robust Visual Features without Supervision. - Nariman Niknejad, Farnaz Adib Yaghmaie, Hamidreza Modares:
Online Reference Tracking For Linear Systems with Unknown Dynamics and Unknown Disturbances. - Daniel Tschernutter, Mathias Kraus, Stefan Feuerriegel:
A Globally Convergent Algorithm for Neural Network Parameter Optimization Based on Difference-of-Convex Functions. - Kefan Su, Zongqing Lu:
A Fully Decentralized Surrogate for Multi-Agent Policy Optimization. - Yi Shen, Pan Xu, Michael M. Zavlanos:
Wasserstein Distributionally Robust Policy Evaluation and Learning for Contextual Bandits. - Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini:
Personalized Algorithmic Recourse with Preference Elicitation. - Saurav Prakash, Jin Sima, Chao Pan, Eli Chien, Olgica Milenkovic:
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex Hulls. - Victor-Alexandru Padurean, Georgios Tzannetos, Adish Singla:
Neural Task Synthesis for Visual Programming. - Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht:
Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning. - Varun A. Kelkar, Rucha Deshpande, Arindam Banerjee, Mark A. Anastasio:
AmbientFlow: Invertible generative models from incomplete, noisy measurements. - Shaoyuan Xie, Zichao Li, Zeyu Wang, Cihang Xie:
On the Adversarial Robustness of Camera-based 3D Object Detection. - Jiayu Zhao, Renyu Yang, Shenghao Qiu, Zheng Wang:
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization. - Burak Varici, Dmitriy Katz, Dennis Wei, Prasanna Sattigeri, Ali Tajer:
Separability Analysis for Causal Discovery in Mixture of DAGs. - Julien Demange-Chryst, François Bachoc, Jérôme Morio, Timothé Krauth:
Variational autoencoder with weighted samples for high-dimensional non-parametric adaptive importance sampling. - David W. Romero, Erik J. Bekkers, Jakub M. Tomczak, Mark Hoogendoorn:
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series. - Shayan Mohajer Hamidi, En-Hui Yang:
AdaFed: Fair Federated Learning via Adaptive Common Descent Direction. - Jitao Lu, Danyang Wu, Feiping Nie, Rong Wang, Xuelong Li:
Hyperspherical Prototype Node Clustering. - Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei:
CAREER: A Foundation Model for Labor Sequence Data. - Shikun Liu, Linxi Fan, Edward Johns, Zhiding Yu, Chaowei Xiao, Anima Anandkumar:
Prismer: A Vision-Language Model with Multi-Task Experts. - Steffen Herbold:
Semantic similarity prediction is better than other semantic similarity measures. - Lukas Balles, Prabhu Teja Sivaprasad, Cédric Archambeau:
On the Choice of Learning Rate for Local SGD. - Vijay Sadashivaiah, Keerthiram Murugesan, Ronny Luss, Pin-Yu Chen, Chris R. Sims, James A. Hendler, Amit Dhurandhar:
To Transfer or Not to Transfer: Suppressing Concepts from Source Representations. - Tamara T. Mueller, Sophie Starck, Alina Dima, Stephan Wunderlich, Kyriaki-Margarita Bintsi, Kamilia Zaripova, Rickmer Braren, Daniel Rueckert, Anees Kazi, Georgios Kaissis:
A Survey on Graph Construction for Geometric Deep Learning in Medicine: Methods and Recommendations. - Meng Liu, Haiyang Yu, Shuiwang Ji:
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm. - Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf:
Towards fully covariant machine learning. - Guanbo Wang, Mireille Schnitzer, Tom Chen, Rui Wang, Robert W. Platt:
A general framework for formulating structured variable selection. - Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen, Hongyu Zhang:
High-dimensional Bayesian Optimization via Covariance Matrix Adaptation Strategy. - Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. - Piyushi Manupriya, Saketha Nath Jagarlapudi, Pratik Jawanpuria:
MMD-Regularized Unbalanced Optimal Transport. - Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel:
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. - Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin:
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling. - Hongyang Yu, Hongjiang C. Yu:
TensorVAE: a simple and efficient generative model for conditional molecular conformation generation. - Lukang Sun, Adil Salim, Peter Richtárik:
Federated Sampling with Langevin Algorithm under Isoperimetry. - Philipp Schiele, Eric Luxenberg, Stephen P. Boyd:
Disciplined Saddle Programming. - Tim Chard, Mark Dras, Paul F. Sowman, Steve Cassidy, Jia Wu:
Temporally Rich Deep Learning Models for Magnetoencephalography. - Shoaib Ahmed Siddiqui, David Krueger, Yann LeCun, Stéphane Deny:
Blockwise Self-Supervised Learning at Scale. - Sameer K. Deshpande, Soumya Ghosh, Tin D. Nguyen, Tamara Broderick:
Are you using test log-likelihood correctly? - Shengchao Liu, Chengpeng Wang, Jiarui Lu, Weili Nie, Hanchen Wang, Zhuoxinran Li, Bolei Zhou, Jian Tang:
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled. - Simar Kareer, Vivek Vijaykumar, Harsh Maheshwari, Judy Hoffman, Prithvijit Chattopadhyay, Viraj Prabhu:
We're Not Using Videos Effectively: An Updated Domain Adaptive Video Segmentation Baseline. - Nathan H. Ng, Ji Won Park, Jae Hyeon Lee, Ryan Lewis Kelly, Stephen Ra, Kyunghyun Cho:
Blind Biological Sequence Denoising with Self-Supervised Set Learning. - William Andersson, Jakob Heiss, Florian Krach, Josef Teichmann:
Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework. - Anirbit Mukherjee, Amartya Roy:
Size Lowerbounds for Deep Operator Networks. - Noriyuki Kojima, Hadar Averbuch-Elor, Yoav Artzi:
A Joint Study of Phrase Grounding and Task Performance in Vision and Language Models. - Young-Kyung Kim, J. Matías Di Martino, Guillermo Sapiro:
Generalizing Neural Additive Models via Statistical Multimodal Analysis. - Jun Yu, Zhaoming Kong, Kun Chen, Xin Zhang, Yong Chen, Lifang He:
A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis. - Zhou Fan, Xinran Han, Zi Wang:
Transfer Learning for Bayesian Optimization on Heterogeneous Search Spaces. - Spyros Gidaris, Andrei Bursuc, Oriane Siméoni, Antonín Vobecký, Nikos Komodakis, Matthieu Cord, Patrick Pérez:
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments. - Angelica Chen, Jason Phang, Alicia Parrish, Vishakh Padmakumar, Chen Zhao, Samuel R. Bowman, Kyunghyun Cho:
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs. - Harsh Pandey, Amitabha Bagchi, Srikanta J. Bedathur, Arindam Bhattacharya:
Data-Dependent Generalization Bounds for Neural Networks with ReLU. - Brendon Boldt, David R. Mortensen:
A Review of the Applications of Deep Learning-Based Emergent Communication. - Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould:
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder. - Yudong Xu, Wenhao Li, Pashootan Vaezipoor, Scott Sanner, Elias Boutros Khalil:
LLMs and the Abstraction and Reasoning Corpus: Successes, Failures, and the Importance of Object-based Representations. - Andrey Davydov, Alexey Sidnev, Artsiom Sanakoyeu, Yuhua Chen, Mathieu Salzmann, Pascal Fua:
Using Motion Cues to Supervise Single-frame Body Pose & Shape Estimation in Low Data Regimes. - Tianlin Liu, Jose Antonio Lara Benitez, Florian Faucher, AmirEhsan Khorashadizadeh, Maarten V. de Hoop, Ivan Dokmanic:
WaveBench: Benchmarking Data-driven Solvers for Linear Wave Propagation PDEs. - Yikai Zhang, Songzhu Zheng, Mina Dalirrooyfard, Pengxiang Wu, Anderson Schneider, Anant Raj, Yuriy Nevmyvaka, Chao Chen:
Learning to Abstain From Uninformative Data. - Guojun Zhang, Mahdi Beitollahi, Alex Bie, Xi Chen:
Understanding the Role of Layer Normalization in Label-Skewed Federated Learning. - Recep Can Yavas, Vincent Y. F. Tan:
Fixed-Budget Best-Arm Identification in Sparse Linear Bandits. - Konstantinos Bousmalis, Giulia Vezzani, Dushyant Rao, Coline Manon Devin, Alex X. Lee, Maria Bauzá Villalonga, Todor Davchev, Yuxiang Zhou, Agrim Gupta, Akhil Raju, Antoine Laurens, Claudio Fantacci, Valentin Dalibard, Martina Zambelli, Murilo Fernandes Martins, Rugile Pevceviciute, Michiel Blokzijl, Misha Denil, Nathan Batchelor, Thomas Lampe, Emilio Parisotto, Konrad Zolna, Scott E. Reed, Sergio Gómez Colmenarejo, Jon Scholz, Abbas Abdolmaleki, Oliver Groth, Jean-Baptiste Regli, Oleg Sushkov, Thomas Rothörl, José Enrique Chen, Yusuf Aytar, Dave Barker, Joy Ortiz, Martin A. Riedmiller, Jost Tobias Springenberg, Raia Hadsell, Francesco Nori, Nicolas Heess:
RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation. - Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum:
Domain-Generalizable Multiple-Domain Clustering. - Ramnath Kumar, Dheeraj Mysore Nagaraj:
Introspective Experience Replay: Look Back When Surprised. - Ran Wei, Nathan Lambert, Anthony D. McDonald, Alfredo García, Roberto Calandra:
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning. - Tal Daniel, Aviv Tamar:
DDLP: Unsupervised Object-centric Video Prediction with Deep Dynamic Latent Particles. - Junhyung Lyle Kim, Gauthier Gidel, Anastasios Kyrillidis, Fabian Pedregosa:
When is Momentum Extragradient Optimal? A Polynomial-Based Analysis. - Shoji Toyota, Kenji Fukumizu:
Out-of-Distribution Optimality of Invariant Risk Minimization. - Justin Singh Kang, Ramtin Pedarsani, Kannan Ramchandran:
The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. - Adit Jain, Vikram Krishnamurthy:
Controlling Federated Learning for Covertness. - Tony Tohme, Mohsen Sadr, Kamal Youcef-Toumi, Nicolas G. Hadjiconstantinou:
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation. - Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Shaofeng Zou, Fei Miao:
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? - Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer:
Hierarchical Neural Simulation-Based Inference Over Event Ensembles. - Albert S. Berahas, Lindon Roberts, Fred Roosta:
Non-Uniform Smoothness for Gradient Descent. - Joe Benton, George Deligiannidis, Arnaud Doucet:
Error Bounds for Flow Matching Methods. - Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite:
Leveraging Function Space Aggregation for Federated Learning at Scale. - Ziyu Jiang, Guoqing Zheng, Yu Cheng, Ahmed Hassan Awadallah, Zhangyang Wang:
CR-MoE: Consistent Routed Mixture-of-Experts for Scaling Contrastive Learning. - Guanghao Li, Wansen Wu, Yan Sun, Li Shen, Baoyuan Wu, Dacheng Tao:
Visual Prompt Based Personalized Federated Learning. - Chuyang Ke, Jean Honorio
:
Provable Guarantees for Sparsity Recovery with Deterministic Missing Data Patterns. - Baturay Saglam, Dogan Can Çiçek, Furkan Burak Mutlu, Suleyman S. Kozat:
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach. - Zhongying Deng, Rihuan Ke, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
NorMatch: Matching Normalizing Flows with Discriminative Classifiers for Semi-Supervised Learning. - W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Gabriele Allievi:
Models of human preference for learning reward functions. - Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio:
Distributional GFlowNets with Quantile Flows. - Adarsh Barik, Jean Honorio:
Recovering Exact Support in Federated lasso without Optimization. - Sheng Qiao, Yong He, Wenxin Zhou:
Transfer Learning for High-dimensional Quantile Regression with Statistical Guarantee. - Matteo Sordello, Niccolò Dalmasso, Hangfeng He, Weijie J. Su:
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic. - Yutaro Yamada, Yihan Bao, Andrew Kyle Lampinen, Jungo Kasai, Ilker Yildirim:
Evaluating Spatial Understanding of Large Language Models. - Zhiwei Zhang, Yuliang Liu:
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation. - Site Bai, Chuyang Ke, Jean Honorio:
On the Dual Problem of Convexified Convolutional Neural Networks. - Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta:
Image Reconstruction via Deep Image Prior Subspaces. - Thomas Markovich:
QDC: Quantum Diffusion Convolution Kernels on Graphs. - Siqi Liu, Andreas Lehrmann:
DynaConF: Dynamic Forecasting of Non-Stationary Time Series. - Jonathan Lee, Weihao Kong, Aldo Pacchiano, Vidya Muthukumar, Emma Brunskill:
Estimating Optimal Policy Value in Linear Contextual Bandits Beyond Gaussianity. - Paul Scemama, Ariel Kapusta:
On the Out-of-Distribution Coverage of Combining Split Conformal Prediction and Bayesian Deep Learning. - Jarrod Haas, William Yolland, Bernhard Rabus:
Exploring Simple, High Quality Out-of-Distribution Detection with L2 Normalization. - Qi Zhao, Qiqi Duan, Bai Yan, Shi Cheng, Yuhui Shi:
Automated Design of Metaheuristic Algorithms: A Survey. - Sonam Gupta, Snehal Singh Tomar, Grigorios Chrysos, Sukhendu Das, Ambasamudram Narayanan Rajagopalan:
PNeRV: A Polynomial Neural Representation for Videos. - Satoshi Hayakawa, Tetsuro Morimura:
Policy Gradient with Kernel Quadrature. - Wanyun Xie, Thomas Pethick, Ali Ramezani-Kebrya, Volkan Cevher:
Mixed Nash for Robust Federated Learning. - Vimal Thilak, Etai Littwin, Shuangfei Zhai, Omid Saremi, Roni Paiss, Joshua M. Susskind:
The Slingshot Effect: A Late-Stage Optimization Anomaly in Adaptive Gradient Methods. - Taman Narayan, Serena Lutong Wang, Kevin Robert Canini, Maya R. Gupta:
Expected Pinball Loss For Quantile Regression And Inverse CDF Estimation. - Andi Han, Dai Shi, Lequan Lin, Junbin Gao:
From Continuous Dynamics to Graph Neural Networks: Neural Diffusion and Beyond. - Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, Thomas L. Griffiths:
Cognitive Architectures for Language Agents. - Leonid Boytsov, Preksha Patel, Vivek Sourabh, Riddhi Nisar, Sayani Kundu, Ramya Ramanathan, Eric Nyberg:
InPars-Light: Cost-Effective Unsupervised Training of Efficient Rankers. - Keliang Li, Hong Chang, Shiguang Shan, Xilin Chen:
Enhancing Robustness to Class-Conditional Distribution Shift in Long-Tailed Recognition. - Matthew D. Kvalheim, Eduardo D. Sontag:
Why should autoencoders work? - Pulkit Gopalani, Samyak Jha, Anirbit Mukherjee:
Global Convergence of SGD For Logistic Loss on Two Layer Neural Nets. - Germán Abrevaya, Mahta Ramezanian-Panahi, Jean-Christophe Gagnon-Audet, Pablo Polosecki, Irina Rish, Silvina Ponce Dawson, Guillermo A. Cecchi, Guillaume Dumas:
Effective Latent Differential Equation Models via Attention and Multiple Shooting. - Linus Aronsson, Morteza Haghir Chehreghani:
Correlation Clustering with Active Learning of Pairwise Similarities. - Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen:
Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation. - Junjie Yin, Jiahao Dong, Yingheng Wang, Christopher De Sa, Volodymyr Kuleshov:
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers. - Pouya M. Ghari, Yanning Shen:
Budgeted Online Model Selection and Fine-Tuning via Federated Learning. - Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. - Bruno Régaldo-Saint Blancard, Michael Eickenberg:
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures. - Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan Ö. Arik, Somesh Jha, Tomas Pfister:
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction. - Atharva Kulkarni, Lucio M. Dery, Amrith Setlur, Aditi Raghunathan, Ameet Talwalkar, Graham Neubig:
Multitask Learning Can Improve Worst-Group Outcomes. - Tamara T. Müller, Sophie Starck, Kyriaki-Margarita Bintsi, Alexander Ziller, Rickmer Braren, Georgios Kaissis, Daniel Rueckert:
Are Population Graphs Really as Powerful as Believed? - Jinyoung Shin, Jae Yong Lee, Hyung Ju Hwang:
Pseudo-Differential Neural Operator: Generalize Fourier Neural operator for Learning Solution Operators of Partial Differential Equations. - Kion Fallah, Alec Helbling, Kyle A. Johnsen, Christopher John Rozell:
Manifold Contrastive Learning with Variational Lie Group Operators. - Angelica Chen, Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Samuel R. Bowman, Kyunghyun Cho, Ethan Perez:
Learning from Natural Language Feedback. - Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han:
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. - Jiahe Lin, Huitian Lei, George Michailidis:
A VAE-based Framework for Learning Multi-Level Neural Granger-Causal Connectivity. - Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert, Georgios Kaissis:
Kernel Normalized Convolutional Networks. - Thomas Frick, Diego Antognini, Ioana Giurgiu, Benjamin F. Grewe, Cristiano Malossi, Rong J. B. Zhu, Mattia Rigotti:
MC Layer Normalization for calibrated uncertainty in Deep Learning. - Shuang Liang, Renata Turkes, Jiayi Li, Nina Otter, Guido Montúfar:
Pull-back Geometry of Persistent Homology Encodings. - Cameron R. Wolfe, Fangshuo Liao, Qihan Wang, Junhyung Lyle Kim, Anastasios Kyrillidis:
How Much Pre-training Is Enough to Discover a Good Subnetwork? - Mohamadsadegh Khosravani, Sandra Zilles:
Using Sum-Product Networks to Assess Uncertainty in Deep Active Learning. - Ziyang Jiang, Tongshu Zheng, Yiling Liu, David E. Carlson:
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel. - Gavin Brown, Riccardo Ali:
Bias/Variance is not the same as Approximation/Estimation. - Qian Zhang, Anuran Makur, Kamyar Azizzadenesheli:
Functional Linear Regression of Cumulative Distribution Functions. - Hongkai Zheng, Weili Nie, Arash Vahdat, Anima Anandkumar:
Fast Training of Diffusion Models with Masked Transformers. - Lucas Mansilla, Estanislao Claucich, Rodrigo Echeveste, Diego H. Milone, Enzo Ferrante:
Demographically-Informed Prediction Discrepancy Index: Early Warnings of Demographic Biases for Unlabeled Populations. - Elre Talea Oldewage, Ross M. Clarke, José Miguel Hernández-Lobato:
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks. - Harsh Vardhan, Avishek Ghosh, Arya Mazumdar:
An Improved Federated Clustering Algorithm with Model-based Clustering. - Long Lian, Boyi Li, Adam Yala, Trevor Darrell:
LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models. - Lucas Rath, Alexander von Rohr, Andreas Schultze, Sebastian Trimpe, Burkhard Corves:
Discovering Model Structure of Dynamical Systems with Combinatorial Bayesian Optimization. - Luis Müller, Mikhail Galkin, Christopher Morris, Ladislav Rampásek:
Attending to Graph Transformers. - Amnon Geifman, Daniel Barzilai, Ronen Basri, Meirav Galun:
Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum. - Adyasha Maharana, Amita Kamath, Christopher Clark, Mohit Bansal, Aniruddha Kembhavi:
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models. - Alexander Tornede, Difan Deng, Theresa Eimer, Joseph Giovanelli, Aditya Mohan, Tim Ruhkopf, Sarah Segel, Daphne Theodorakopoulos, Tanja Tornede, Henning Wachsmuth, Marius Lindauer:
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. - Francesco Di Giovanni, T. Konstantin Rusch, Michael M. Bronstein, Andreea Deac, Marc Lackenby, Siddhartha Mishra, Petar Velickovic:
How does over-squashing affect the power of GNNs? - Rohan Asthana, Joschua Conrad, Youssef Dawoud, Maurits Ortmanns, Vasileios Belagiannis:
Multi-conditioned Graph Diffusion for Neural Architecture Search. - Nikolas Adaloglou, Felix Michels, Tim Kaiser, Markus Kollmann:
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection. - Marc Rigter, Jun Yamada, Ingmar Posner:
World Models via Policy-Guided Trajectory Diffusion. - Guillaume Staerman, Pavlo Mozharovskyi, Pierre Colombo, Stéphan Clémençon, Florence d'Alché-Buc:
A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions. - Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio:
Improving and generalizing flow-based generative models with minibatch optimal transport. - Xiaoyang Wang, Han Zhao, Klara Nahrstedt, Sanmi Koyejo:
Personalized Federated Learning with Spurious Features: An Adversarial Approach. - Wei Duan, Jie Lu, Yu Guang Wang, Junyu Xuan:
Layer-diverse Negative Sampling for Graph Neural Networks. - Gennaro Gala, Daniele Grattarola, Erik Quaeghebeur:
E(n)-equivariant Graph Neural Cellular Automata. - Akshaj Kumar Veldanda, Ivan Brugere, Sanghamitra Dutta, Alan Mishler, Siddharth Garg:
Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access. - Thomas Kehrenberg, Myles Bartlett, Viktoriia Sharmanska, Novi Quadrianto:
Addressing Attribute Bias with Adversarial Support-Matching. - Akash Saha, Balamurugan Palaniappan:
Learning Sparse Graphs for Functional Regression using Graph-induced Operator-valued Kernels. - Yae Jee Cho, Divyansh Jhunjhunwala, Tian Li, Virginia Smith, Gauri Joshi:
Maximizing Global Model Appeal in Federated Learning. - Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, Kangwook Lee:
Predictive Pipelined Decoding: A Compute-Latency Trade-off for Exact LLM Decoding. - Caleb Chuck, Kevin Black, Aditya Arjun, Yuke Zhu, Scott Niekum:
Granger Causal Interaction Skill Chains. - Dongruo Zhou, Jinghui Chen, Yuan Cao, Ziyan Yang, Quanquan Gu:
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization. - Chengrui Li, Anqi Wu:
Inverse Kernel Decomposition. - Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao, Junbin Gao:
Revisiting Generalized p-Laplacian Regularized Framelet GCNs: Convergence, Energy Dynamic and as Non-Linear Diffusion. - Deyao Zhu, Jun Chen, Kilichbek Haydarov, Xiaoqian Shen, Wenxuan Zhang, Mohamed Elhoseiny:
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions. - Chenshuang Zhang, Chaoning Zhang, Kang Zhang, Axi Niu, Junmo Kim, In So Kweon:
Towards Understanding Dual BN In Hybrid Adversarial Training. - Minh H. Vu, Anders Garpebring, Tufve Nyholm, Tommy Löfstedt:
Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect. - Jack W. Miller, Charles O'Neill, Thang D. Bui:
Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity. - O. Duranthon, Lenka Zdeborová:
Optimal Inference in Contextual Stochastic Block Models. - Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, Anima Anandkumar:
Voyager: An Open-Ended Embodied Agent with Large Language Models. - Md. Mahadi Hassan, Alex Knipper, Shubhra Kanti Karmaker Santu:
Introducing "Forecast Utterance" for Conversational Data Science. - Shaozhe Hao, Kai Han, Kwan-Yee K. Wong:
CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery. - Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
From Differential Privacy to Bounds on Membership Inference: Less can be More. - Chinmaya Kausik, Kashvi Srivastava, Rishi Sonthalia:
Double Descent and Overfitting under Noisy Inputs and Distribution Shift for Linear Denoisers. - Tao Li, Qinghua Tao, Weihao Yan, Yingwen Wu, Zehao Lei, Kun Fang, Mingzhen He, Xiaolin Huang:
Revisiting Random Weight Perturbation for Efficiently Improving Generalization. - Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani K. Mallick:
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World. - Maxwell G. Anderson, Shi-Yuan Ma, Tianyu Wang, Logan G. Wright, Peter L. McMahon:
Optical Transformers. - Maria-Florina Balcan, Hedyeh Beyhaghi:
New Guarantees for Learning Revenue Maximizing Menus of Lotteries and Two-Part Tariffs. - James Queeney, Erhan Can Ozcan, Ioannis Ch. Paschalidis, Christos G. Cassandras:
Optimal Transport Perturbations for Safe Reinforcement Learning with Robustness Guarantees. - Laurence Aitchison, Stoil Ganev:
InfoNCE is variational inference in a recognition parameterised model. - Yuhta Takida, Yukara Ikemiya, Takashi Shibuya, Kazuki Shimada, Woosung Choi, Chieh-Hsin Lai, Naoki Murata, Toshimitsu Uesaka, Kengo Uchida, Wei-Hsiang Liao, Yuki Mitsufuji:
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes. - Julius Berner, Lorenz Richter, Karen Ullrich:
An optimal control perspective on diffusion-based generative modeling. - Paul Couairon, Clément Rambour, Jean-Emmanuel Haugeard, Nicolas Thome:
VidEdit: Zero-Shot and Spatially Aware Text-Driven Video Editing. - Ashwani Aggarwal:
Convergence Analysis of Fractional Gradient Descent. - Honglin Chen, Wanhee Lee, Hong-Xing Yu, Rahul Mysore Venkatesh, Joshua B. Tenenbaum, Daniel Bear, Jiajun Wu, Daniel L. K. Yamins:
Unsupervised 3D Scene Representation Learning via Movable Object Inference. - Prateek Yadav, Peter Hase, Mohit Bansal:
INSPIRE: Incorporating Diverse Feature Preferences in Recourse. - Derek Tam, Mohit Bansal, Colin Raffel:
Merging by Matching Models in Task Parameter Subspaces. - Haoxiang Wang, Zhanhong Jiang, Chao Liu, Soumik Sarkar, Dongxiang Jiang, Young M. Lee:
Asynchronous Training Schemes in Distributed Learning with Time Delay. - Tiancheng Qin, S. Rasoul Etesami, César A. Uribe:
Faster Convergence of Local SGD for Over-Parameterized Models. - Yue Kang, Cho-Jui Hsieh, Thomas C. M. Lee:
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits. - Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Tom Goldstein, David Wipf:
Graph Neural Networks Formed via Layer-wise Ensembles of Heterogeneous Base Models. - Bo Li, Yasin Esfandiari, Mikkel N. Schmidt, Tommy Sonne Alstrøm, Sebastian U. Stich:
Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity. - Zhuotong Chen, Zihu Wang, Yifan Yang, Qianxiao Li, Zheng Zhang:
PID Control-Based Self-Healing to Improve the Robustness of Large Language Models. - Jie Bian, Vincent Y. F. Tan:
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits. - Yoann Boget, Magda Gregorova, Alexandros Kalousis:
Discrete Graph Auto-Encoder. - Jingtong Su, Ya Shi Zhang, Nikolaos Tsilivis, Julia Kempe:
On the Robustness of Neural Collapse and the Neural Collapse of Robustness. - Martin Ferianc, Ondrej Bohdal, Timothy M. Hospedales, Miguel R. D. Rodrigues:
Navigating Noise: A Study of How Noise Influences Generalisation and Calibration of Neural Networks. - El Mahdi Chayti, Sai Praneeth Karimireddy:
Optimization with Access to Auxiliary Information. - Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Tales Imbiriba, Eugene Tunik, Deniz Erdogmus, Mathew Yarossi, Robin Walters:
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder. - Gaotang Li, Jiarui Liu, Wei Hu:
Bias Amplification Enhances Minority Group Performance. - Sakshi Choudhary, Sai Aparna Aketi, Gobinda Saha, Kaushik Roy:
CoDeC: Communication-Efficient Decentralized Continual Learning. - Sergio Calvo-Ordoñez, Chun-Wun Cheng, Jiahao Huang, Lipei Zhang, Guang Yang, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
The Missing U for Efficient Diffusion Models. - Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien:
PopulAtion Parameter Averaging (PAPA). - Semih Cayci, Niao He, R. Srikant:
Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm. - Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong Liu:
Understanding Fairness Surrogate Functions in Algorithmic Fairness. - Hongyi Yuan, Songchi Zhou, Sheng Yu:
EHRDiff : Exploring Realistic EHR Synthesis with Diffusion Models. - Brian Chen, Doruk Aksoy, David J. Gorsich, Shravan K. Veerapaneni, Alex A. Gorodetsky:
Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning. - Zichao Li, Cihang Xie, Ekin Dogus Cubuk:
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies. - Konstantin Gasenzer, Moritz Wolter:
Towards generalizing deep-audio fake detection networks. - Anshuman Sinha, Spencer H. Bryngelson:
Neural networks can be FLOP-efficient integrators of 1D oscillatory integrands. - Nicolas Deutschmann, Mattia Rigotti, María Rodríguez Martínez:
Adaptive Conformal Regression with Split-Jackknife+ Scores. - Zachary Izzo, Jinsung Yoon, Sercan Ö. Arik, James Zou:
Provable Membership Inference Privacy. - Weiye Zhao, Rui Chen, Yifan Sun, Feihan Li, Tianhao Wei, Changliu Liu:
State-wise Constrained Policy Optimization. - Prafulla Chandra, Andrew Thangaraj, Nived Rajaraman:
How good is Good-Turing for Markov samples? - Shuqi Lu, Lin Yao, Xi Chen, Hang Zheng, Di He, Guolin Ke:
3D Molecular Generation via Virtual Dynamics. - Tom S. F. Haines:
The Cross-entropy of Piecewise Linear Probability Density Functions. - Dingfan Chen, Raouf Kerkouche, Mario Fritz:
A Unified View of Differentially Private Deep Generative Modeling. - Thuan Nguyen Anh Trang, Nhat Khang Ngo, Hugo Sonnery, Thieu N. Vo, Siamak Ravanbakhsh, Truong Son Hy:
Scalable Hierarchical Self-Attention with Learnable Hierarchy for Long-Range Interactions. - Daniel Morales-Brotons, Thijs Vogels, Hadrien Hendrikx:
Exponential Moving Average of Weights in Deep Learning: Dynamics and Benefits. - Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb:
Anomaly detection with semi-supervised classification based on risk estimators. - Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. - Ozan Özdenizci, Robert Legenstein:
Adversarially Robust Spiking Neural Networks Through Conversion. - Minghua Wang, Yan Hu, Ziyun Huang, Di Wang, Jinhui Xu:
Persistent Local Homology in Graph Learning. - Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi:
Incremental Extractive Opinion Summarization Using Cover Trees. - Weiye Zhao, Yifan Sun, Feihan Li, Rui Chen, Ruixuan Liu, Tianhao Wei, Changliu Liu:
GUARD: A Safe Reinforcement Learning Benchmark. - Joseph Pemberton, Rui Ponte Costa:
BP(λ): Online Learning via Synthetic Gradients. - James Seale Smith, Yen-Chang Hsu, Lingyu Zhang, Ting Hua, Zsolt Kira, Yilin Shen, Hongxia Jin:
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA. - Lucas Hayne, Heejung Jung, R. McKell Carter:
Does Representation Similarity Capture Function Similarity? - Karanpartap Singh, James Zou:
New Evaluation Metrics Capture Quality Degradation due to LLM Watermarking. - Iason Skylitsis, Zheng Feng, Idries Nasim, Camille Niessink:
Reproducibility Study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework". - Eugenio Clerico, Benjamin Guedj:
A note on regularised NTK dynamics with an application to PAC-Bayesian training. - Bahjat Kawar, Noam Elata, Tomer Michaeli, Michael Elad:
GSURE-Based Diffusion Model Training with Corrupted Data. - Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang:
MUBen: Benchmarking the Uncertainty of Molecular Representation Models. - Alain Rakotomamonjy, Maxime Vono, Hamlet Jesse Medina Ruiz, Liva Ralaivola:
Personalised Federated Learning On Heterogeneous Feature Spaces. - Puneesh Deora, Rouzbeh Ghaderi, Hossein Taheri, Christos Thrampoulidis:
On the Optimization and Generalization of Multi-head Attention. - Nikita Durasov, Nik Dorndorf, Hieu Le, Pascal Fua:
ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference. - Avi Singh, John D. Co-Reyes, Rishabh Agarwal, Ankesh Anand, Piyush Patil, Xavier Garcia, Peter J. Liu, James Harrison, Jaehoon Lee, Kelvin Xu, Aaron T. Parisi, Abhishek Kumar, Alexander A. Alemi, Alex Rizkowsky, Azade Nova, Ben Adlam, Bernd Bohnet, Gamaleldin Fathy Elsayed, Hanie Sedghi, Igor Mordatch, Isabelle Simpson, Izzeddin Gur, Jasper Snoek, Jeffrey Pennington, Jiri Hron, Kathleen Kenealy, Kevin Swersky, Kshiteej Mahajan, Laura Culp, Lechao Xiao, Maxwell L. Bileschi, Noah Constant, Roman Novak, Rosanne Liu, Tris Warkentin, Yundi Qian, Yamini Bansal, Ethan Dyer, Behnam Neyshabur, Jascha Sohl-Dickstein, Noah Fiedel:
Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models. - Xiang Li, Qiang Sun:
Variance-aware decision making with linear function approximation under heavy-tailed rewards. - Corrado Monti, Paolo Bajardi, Francesco Bonchi, André Panisson, Alan Perotti:
A True-to-the-model Axiomatic Benchmark for Graph-based Explainers. - Matthias Freiberger, Peter Kun, Christian Igel, Anders Sundnes Løvlie, Sebastian Risi:
Fooling Contrastive Language-Image Pre-Trained Models with CLIPMasterPrints. - John Thickstun, David Leo Wright Hall, Chris Donahue, Percy Liang:
Anticipatory Music Transformer. - Jixuan Leng, Yijiang Li, Haohan Wang:
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization. - Habibur Rahman, Thirupathi Reddy Emireddy, Kenneth Tjhia, Elham Parhizkar, Levi Lelis:
Synthesizing Libraries of Programs with Auxiliary Functions. - Etienne Le Naour, Louis Serrano, Léon Migus, Yuan Yin, Ghislain Agoua, Nicolas Baskiotis, Patrick Gallinari, Vincent Guigue:
Time Series Continuous Modeling for Imputation and Forecasting with Implicit Neural Representations. - Talay M. Cheema, Carl Edward Rasmussen:
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes. - Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? - Mohammad Alkhalefi, Georgios Leontidis, Mingjun Zhong:
Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods. - Teodora Pandeva, Tim Bakker, Christian A. Naesseth, Patrick Forré:
E-Valuating Classifier Two-Sample Tests. - Jing Wang, Wonho Bae, Jiahong Chen, Kuangen Zhang, Leonid Sigal, Clarence W. de Silva:
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context. - Arnaud Pannatier, Kyle Matoba, François Fleuret:
Inference from Real-World Sparse Measurements. - Ayoub Belhadji, Rémi Gribonval:
Sketch and shift: a robust decoder for compressive clustering. - Tianlin Liu, Mathieu Blondel, Carlos Riquelme Ruiz, Joan Puigcerver:
Routers in Vision Mixture of Experts: An Empirical Study. - Juliette Achddou, Olivier Cappé, Aurélien Garivier:
Stochastic Direct Search Methods for Blind Resource Allocation. - Mufan Bill Li, Mihai Nica:
Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks. - Elia Cunegatti, Matteo Farina, Doina Bucur, Giovanni Iacca:
Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations. - Peiran Xu, Zeyu Wang, Jieru Mei, Liangqiong Qu, Alan L. Yuille, Cihang Xie, Yuyin Zhou:
FedConv: Enhancing Convolutional Neural Networks for Handling Data Heterogeneity in Federated Learning. - Wenkai Yang, Deli Chen, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun:
Decentralized Decoupled Training for Federated Long-Tailed Learning. - Chun-Wun Cheng, Christina Runkel, Lihao Liu, Raymond H. Chan, Carola-Bibiane Schönlieb, Angelica I. Avilés-Rivero:
Continuous U-Net: Faster, Greater and Noiseless. - Jiabao Ji, Guanhua Zhang, Zhaowen Wang, Bairu Hou, Zhifei Zhang, Brian L. Price, Shiyu Chang:
Improving Diffusion Models for Scene Text Editing with Dual Encoders. - Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, Laura Toni:
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning. - Baran Ozaydin, Tong Zhang, Sabine Süsstrunk, Mathieu Salzmann:
DSI2I: Dense Style for Unpaired Exemplar-based Image-to- Image Translation. - Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon:
Identify Ambiguous Tasks Combining Crowdsourced Labels by Weighting Areas Under the Margin. - Clio Feng, Colin Bot, Bart den Boef, Bart Aaldering:
Reproducibility Study of "Explaining RL Decisions with Trajectories". - Lorenzo Luzi, Helen Jenne, Carlos Ortiz Marrero, Ryan Murray:
Using Skew to Assess the Quality of GAN-generated Image Features. - Xuxing Chen, Krishna Balasubramanian, Promit Ghosal, Bhavya Agrawalla:
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression. - Aleksandar Stanic, Sergi Caelles, Michael Tschannen:
Towards Truly Zero-shot Compositional Visual Reasoning with LLMs as Programmers. - Efstathia Soufleri, Deepak Ravikumar, Kaushik Roy:
DP-ImgSyn: Dataset Alignment for Obfuscated, Differentially Private Image Synthesis. - Nurudin Alvarez-Gonzalez, Andreas Kaltenbrunner, Vicenç Gómez:
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings. - Prashant Shivaram Bhat, Bharath Renjith, Elahe Arani, Bahram Zonooz:
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning. - Daniel Waxman, Petar M. Djuric:
Dynamic Online Ensembles of Basis Expansions. - Juliette Marrie, Michael Arbel, Julien Mairal, Diane Larlus:
On Good Practices for Task-Specific Distillation of Large Pretrained Visual Models. - Jack Blandin, Ian A. Kash:
Group Fairness in Reinforcement Learning via Multi-Objective Rewards. - Chuan He, Le Peng, Ju Sun:
Federated Learning with Convex Global and Local Constraints. - Max Vladymyrov, Andrey Zhmoginov, Mark Sandler:
Continual HyperTransformer: A Meta-Learner for Continual Few-Shot Learning. - Hikari Otsuka, Yasuyuki Okoshi, Ángel López García-Arias, Kazushi Kawamura, Thiem Van Chu, Daichi Fujiki, Masato Motomura:
Restricted Random Pruning at Initialization for High Compression Range. - Debamita Ghosh, Manjesh Kumar Hanawal, Nikola Zlatanov:
Fixed Budget Best Arm Identification in Unimodal Bandits. - Xinwei Zhang, Wotao Yin, Mingyi Hong, Tianyi Chen:
Hybrid Federated Learning for Feature & Sample Heterogeneity: Algorithms and Implementation. - Peimeng Guan, Naveed Iqbal, Mark A. Davenport, Mudassir Masood:
Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures. - Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqian Yu, Xinwei Liu, Avery Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip Torr:
A Survey on Transferability of Adversarial Examples Across Deep Neural Networks. - Lingwei Zhu, Matthew Schlegel, Han Wang, Martha White:
Offline Reinforcement Learning via Tsallis Regularization. - Saber Salehkaleybar, Mohammadsadegh Khorasani, Negar Kiyavash, Niao He, Patrick Thiran:
Momentum-Based Policy Gradient with Second-Order Information. - Valay Bundele, Mahesh Bhupati, Biplab Banerjee, Aditya Grover:
Scaling Vision-and-Language Navigation With Offline RL. - Zi Yang, Haojin Yang, Soumajit Majumder, Jorge Cardoso, Guillermo Gallego:
Data Pruning Can Do More: A Comprehensive Data Pruning Approach for Object Re-identification. - Nesta Midavaine, Gregory Hok Tjoan Go, Diego Canez, Ioana Simion, Satchit Chatterji:
[Re] On the Reproducibility of Post-Hoc Concept Bottleneck Models. - Sebastian Mair, Jens Sjölund:
Archetypal Analysis++: Rethinking the Initialization Strategy. - Abdulla Jasem Almansoori, Samuel Horváth, Martin Takác:
PaDPaF: Partial Disentanglement with Partially-Federated GANs. - Bas van der Heijden, Laura Ferranti, Jens Kober, Robert Babuska:
Efficient Parallelized Simulation of Cyber-Physical Systems. - Zhaoyu Li, Jinpei Guo, Xujie Si:
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks. - Samidha Verma, Burouj Armgaan, Sourav Medya, Sayan Ranu:
InduCE: Inductive Counterfactual Explanations for Graph Neural Networks. - Denis Huseljic, Marek Herde, Yannick Nagel, Lukas Rauch, Paulius Strimaitis, Bernhard Sick:
The Interplay of Uncertainty Modeling and Deep Active Learning: An Empirical Analysis in Image Classification. - Zihan Li, Jonathan Scarlett:
Regret Bounds for Noise-Free Cascaded Kernelized Bandits. - Charles Arnal, Felix Hensel, Mathieu Carrière, Théo Lacombe, Hiroaki Kurihara, Yuichi Ike, Frédéric Chazal:
MAGDiff: Covariate Data Set Shift Detection via Activation Graphs of Neural Networks. - Emilio Porcu, Ana Paula Peron, Eugenio Massa, Xavier Emery:
Understanding Smoothness of Vector Gaussian Processes on Product Spaces. - Diana Gomes, Kyriakos Efthymiadis, Ann Nowé, Peter Vrancx:
Depth Scaling in Graph Neural Networks: Understanding the Flat Curve Behavior. - Masanari Kimura, Hideitsu Hino:
A Short Survey on Importance Weighting for Machine Learning. - Mianchu Wang, Rui Yang, Xi Chen, Hao Sun, Meng Fang, Giovanni Montana:
GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models. - Simona Ioana Juvina, Ana Antonia Neacsu, Jérôme Rony, Jean-Christophe Pesquet, Corneliu Burileanu, Ismail Ben Ayed:
Training Graph Neural Networks Subject to a Tight Lipschitz Constraint. - Xinwei Zhang, Mingyi Hong, Jie Chen:
GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data. - Daria Bystrova, Charles K. Assaad, Julyan Arbel, Emilie Devijver, Éric Gaussier, Wilfried Thuiller:
Causal Discovery from Time Series with Hybrids of Constraint-Based and Noise-Based Algorithms. - Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Carola-Bibiane Schönlieb:
Boosting Data-Driven Mirror Descent with Randomization, Equivariance, and Acceleration. - David I. Müller, Jimmy Aronsson, Daniel Schuh:
Geometrical aspects of lattice gauge equivariant convolutional neural networks. - Anton Frederik Thielmann, Arik Reuter, Thomas Kneib, David Rügamer, Benjamin Säfken:
Interpretable Additive Tabular Transformer Networks. - Karthik Somayaji N. S., Yu Wang, Malachi Schram, Ján Drgona, Mahantesh M. Halappanavar, Frank Liu, Peng Li:
Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory. - Jiapeng Fan, Luke Cadigan, Paulius Skaisgiris, Sebastian Uriel Arias:
Reproducibility Study of "Learning Perturbations to Explain Time Series Predictions". - Dongfu Jiang, Yishan Li, Ge Zhang, Wenhao Huang, Bill Yuchen Lin, Wenhu Chen:
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks. - Julia Grabinski, Janis Keuper, Margret Keuper:
As large as it gets - Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters. - Zhou Wang, Xingye Qiao:
Deep Generalized Prediction Set Classifier and Its Theoretical Guarantees. - Zhongwei Wan, Xin Wang, Che Liu, Samiul Alam, Yu Zheng, Jiachen Liu, Zhongnan Qu, Shen Yan, Yi Zhu, Quanlu Zhang, Mosharaf Chowdhury, Mi Zhang:
Efficient Large Language Models: A Survey. - Miklós Hamar, Matey Krastev, Kristiyan Danielov Hristov, David Beglou:
[Re] Explaining Temporal Graph Models through an Explorer-Navigator Framework. - Pratik Jawanpuria, Bamdev Mishra, Karthik S. Gurumoorthy:
Revisiting stochastic submodular maximization with cardinality constraint: A bandit perspective. - Amirhosein Ghasemabadi, Muhammad Kamran Janjua, Mohammad Salameh, Chunhua Zhou, Fengyu Sun, Di Niu:
CascadedGaze: Efficiency in Global Context Extraction for Image Restoration. - Qiujiang Jin, Tongzheng Ren, Nhat Ho, Aryan Mokhtari:
Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models. - Haoxiang Wang, Haozhe Si, Huajie Shao, Han Zhao:
Enhancing Compositional Generalization via Compositional Feature Alignment. - Yaochen Xie, Ziqian Xie, Sheikh Muhammad Saiful Islam, Degui Zhi, Shuiwang Ji:
Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies. - Aritra Mitra, George J. Pappas, Hamed Hassani:
Temporal Difference Learning with Compressed Updates: Error-Feedback meets Reinforcement Learning. - Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen:
From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling. - Ethan Harvey, Mikhail Petrov, Michael C. Hughes:
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported. - Vittorio Giammarino, James Queeney, Ioannis Ch. Paschalidis:
Adversarial Imitation Learning from Visual Observations using Latent Information. - Jeremy Vonderfecht, Feng Liu:
Predicting the Encoding Error of SIRENs. - Ning Lu, Shengcai Liu, Rui He, Yew-Soon Ong, Qi Wang, Ke Tang:
Large Language Models can be Guided to Evade AI-generated Text Detection. - Margarita Vinaroz, Mijung Park:
Differentially Private Kernel Inducing Points using features from ScatterNets (DP-KIP-ScatterNet) for Privacy Preserving Data Distillation. - Pengyuan Lyu, Chengquan Zhang, Shanshan Liu, Meina Qiao, Yangliu Xu, Liang Wu, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang:
MaskOCR: Scene Text Recognition with Masked Vision-Language Pre-training. - Mohammed Muqeeth, Haokun Liu, Colin Raffel:
Soft Merging of Experts with Adaptive Routing. - Jihao Liu, Jinliang Zheng, Boxiao Liu, Yu Liu, Hongsheng Li:
Enhancing Vision-Language Model with Unmasked Token Alignment. - Zhen Lin, Shubhendu Trivedi, Jimeng Sun:
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models. - Tianhao Wei, Ziwei Wang, Peizhi Niu, Abulikemu Abuduweili, Weiye Zhao, Casidhe Hutchison, Eric M. Sample, Changliu Liu:
Improve Certified Training with Signal-to-Noise Ratio Loss to Decrease Neuron Variance and Increase Neuron Stability. - Fatemeh Nourilenjan Nokabadi, Jean-François Lalonde, Christian Gagné:
Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers. - Keiichiro Yamamura, Issa Oe, Nozomi Hata, Hiroki Ishikura, Katsuki Fujisawa:
Appropriate Balance of Diversification and Intensification Improves Performance and Efficiency of Adversarial Attacks. - Mila Gorecki, Jakob H. Macke, Michael Deistler:
Amortized Bayesian Decision Making for simulation-based models. - Mohammadreza Salehi, Mehrdad Farajtabar, Maxwell Horton, Fartash Faghri, Hadi Pouransari, Raviteja Vemulapalli, Oncel Tuzel, Ali Farhadi, Mohammad Rastegari, Sachin Mehta:
CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement. - Royden Wagner, Ömer Sahin Tas, Marvin Klemp, Carlos Fernández, Christoph Stiller:
RedMotion: Motion Prediction via Redundancy Reduction. - Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan L. Yuille, Yuyin Zhou, Cihang Xie:
Unleashing the Power of Visual Prompting At the Pixel Level. - Cencheng Shen, Jaewon Chung, Ronak D. Mehta, Ting Xu, Joshua T. Vogelstein:
Independence Testing for Temporal Data. - Rémi Kazmierczak, Eloïse Berthier, Goran Frehse, Gianni Franchi:
CLIP-QDA: An Explainable Concept Bottleneck Model. - Mariano Tepper, Ishwar Singh Bhati, Cecilia Aguerrebere, Mark Hildebrand, Theodore L. Willke:
LeanVec: Searching vectors faster by making them fit. - Marloes Arts, Jes Frellsen, Wouter Boomsma:
Internal-Coordinate Density Modelling of Protein Structure: Covariance Matters. - Miguel de Carvalho, Gabriel Martos Venturini:
Uncovering Sets of Maximum Dissimilarity on Random Process Data. - Diaaeldin Taha, Wei Zhao, J. Maxwell Riestenberg, Michael Strube:
Normed Spaces for Graph Embedding. - Klára Janousková, Tamir Shor, Chaim Baskin, Jiri Matas:
Single Image Test-Time Adaptation for Segmentation. - Zhehao Huang, Tao Li, Chenhe Yuan, Yingwen Wu, Xiaolin Huang:
Online Continual Learning via Logit Adjusted Softmax. - Rongzhe Wei, Eleonora Kreacic, Haoyu Peter Wang, Haoteng Yin, Eli Chien, Vamsi K. Potluru, Pan Li:
On the Inherent Privacy Properties of Discrete Denoising Diffusion Models. - Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman:
Certified Deductive Reasoning with Language Models. - Weijie Tu, Weijian Deng, Liang Zheng, Tom Gedeon:
What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions? - Jan Tönshoff, Martin Ritzert, Eran Rosenbluth, Martin Grohe:
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark. - Keitaro Sakamoto, Issei Sato:
End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training. - Pratik Vaishnavi, Kevin Eykholt, Amir Rahmati:
A Study of the Effects of Transfer Learning on Adversarial Robustness. - Tong Wu:
Online Tensor Max-Norm Regularization via Stochastic Optimization. - Karim Abdel Sadek, Matteo Nulli, Joan Velja, Jort Vincenti:
'Explaining RL Decisions with Trajectories': A Reproducibility Study. - Francesco Pedrotti, Jan Maas, Marco Mondelli:
Improved Convergence of Score-Based Diffusion Models via Prediction-Correction. - Ziyi Wang, Yujie Chen, Qifan Song, Ruqi Zhang:
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo. - Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, Percy Liang:
Robust Distortion-free Watermarks for Language Models. - Luan Fletcher, Robert van der Klis, Martin Sedlácek, Stefan Vasilev, Christos Athanasiadis:
Reproducibility study of "LICO: Explainable Models with Language-Image Consistency". - Ayush Agrawal, Raghav Prabhakar, Anirudh Goyal, Dianbo Liu:
Physical Reasoning and Object Planning for Household Embodied Agents. - Caroline Choi, Fahim Tajwar, Yoonho Lee, Huaxiu Yao, Ananya Kumar, Chelsea Finn:
Conservative Prediction via Data-Driven Confidence Minimization. - Srishti Gautam, Ahcene Boubekki, Marina M.-C. Höhne, Michael Kampffmeyer:
Prototypical Self-Explainable Models Without Re-training. - Zexian Huang, Kourosh Khoshelham, Martin Tomko:
Contrastive Graph Autoencoder for Shape-based Polygon Retrieval from Large Geometry Datasets. - Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola:
Multimodal Chain-of-Thought Reasoning in Language Models. - Moshe Kimhi, Shai Kimhi, Evgenii Zheltonozhskii, Or Litany, Chaim Baskin:
Semi-Supervised Semantic Segmentation via Marginal Contextual Information. - Zhili Feng, Anna Bair, J. Zico Kolter:
Text Descriptions are Compressive and Invariant Representations for Visual Learning. - Pierre Erbacher, Jian-Yun Nie, Philippe Preux, Laure Soulier:
Augmenting Ad-Hoc IR Dataset for Interactive Conversational Search. - Kedar Karhadkar, Michael Murray, Hanna Tseran, Guido Montúfar:
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape. - Yiyan Huang, Siyi Wang, Cheuk Hang Leung, Qi Wu, Dongdong Wang, Zhixiang Huang:
DIGNet: Learning Decomposed Patterns in Representation Balancing for Treatment Effect Estimation. - Dimitris Bertsimas, Arthur Delarue, Jean Pauphilet:
Simple Imputation Rules for Prediction with Missing Data: Theoretical Guarantees vs. Empirical Performance. - Giulia Clerici, Pierre Laforgue, Nicolò Cesa-Bianchi:
Linear Bandits with Memory. - Albert Ziegler, Pawel Czyz:
Bayesian Quantification with Black-Box Estimators. - Nancy Nayak, Sheetal Kalyani:
Rotate the ReLU to Sparsify Deep Networks Implicitly. - Ryan P. Kelly, David J. Nott, David T. Frazier, David J. Warne, Christopher C. Drovandi:
Misspecification-robust Sequential Neural Likelihood for Simulation-based Inference. - Yihong Guo, Hao Liu, Yisong Yue, Anqi Liu:
Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits. - Ana Vasilcoiu, T. H. F. Stessen, Thies Kersten, Batu Helvacioglu:
[Re] GNNInterpreter: A probabilistic generative model-level explanation for Graph Neural Networks. - Lars Doorenbos, Pablo Márquez-Neila, Raphael Sznitman, Pascal Mettes:
Hyperbolic Random Forests. - Muberra Ozmen, Thomas Markovich:
Recent Link Classification on Temporal Graphs Using Graph Profiler. - Seojin Kim, Jaehyun Nam, Junsu Kim, Hankook Lee, Sungsoo Ahn, Jinwoo Shin:
Holistic Molecular Representation Learning via Multi-view Fragmentation. - Alexander D. J. Taylor, Phillip Tregidgo, Jonathan James Morrison, Neill D. F. Campbell:
VisionAD, a software package of performant anomaly detection algorithms, and Proportion Localised, an interpretable metric. - Tianqi Zhao, Alan Hanjalic, Megha Khosla:
AGALE: A Graph-Aware Continual Learning Evaluation Framework. - Xiao Li, Sheng Liu, Jinxin Zhou, Xinyu Lu, Carlos Fernandez-Granda, Zhihui Zhu, Qing Qu:
Understanding and Improving Transfer Learning of Deep Models via Neural Collapse. - Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon, Dhananjaya N. Gowda, Chanwoo Kim, Chang D. Yoo:
Physics Informed Distillation for Diffusion Models. - Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar:
Promoting Exploration in Memory-Augmented Adam using Critical Momenta. - Yue Wang, Jinjun Xiong, Shaofeng Zou:
Achieving the Asymptotically Minimax Optimal Sample Complexity of Offline Reinforcement Learning: A DRO-Based Approach. - Helia Ghasemi, Christina Isaicu, Jesse Wonnink, Andreas Berentzen:
[Re] Reproducibility Study of "Explaining Temporal Graph Models Through an Explorer-Navigator Framework". - Giovanni Minelli, Mirco Musolesi:
CoMIX: A Multi-agent Reinforcement Learning Training Architecture for Efficient Decentralized Coordination and Independent Decision-Making. - Takashi Furuya, Satoshi Okuda, Kazuma Suetake, Yoshihide Sawada:
Convergences for Minimax Optimization Problems over Infinite-Dimensional Spaces Towards Stability in Adversarial Training. - Ryoma Sato:
Making Translators Privacy-aware on the User's Side. - Aahlad Manas Puli, Nitish Joshi, Yoav Wald, He He, Rajesh Ranganath:
Nuisances via Negativa: Adjusting for Spurious Correlations via Data Augmentation. - Yinan He, Lile Cai, Jingyi Liao, Chuan-Sheng Foo:
Hybrid Active Learning with Uncertainty-Weighted Embeddings. - Barath Chandran C.:
[Re] CUDA: Curriculum of Data Augmentation for Long-tailed Recognition. - Thomas Rodrigues Crespo, Jun-nosuke Teramae:
Smoothed Robustness Analysis: Bridging worst- and average-case robustness analyses via smoothed analysis. - Jonathan Pirnay, Dominik G. Grimm:
Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement. - Abhilash Nandy, Manav Nitin Kapadnis, Sohan Patnaik, Yash Parag Butala, Pawan Goyal, Niloy Ganguly:
***FastDoc***: Domain-Specific Fast Continual Pre-training Technique using Document-Level Metadata and Taxonomy. - Ali Devran Kara, Serdar Yüksel:
Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments. - Louis Filstroff, Iiris Sundin, Petrus Mikkola, Aleksei Tiulpin, Juuso Kylmäoja, Samuel Kaski:
Targeted Active Learning for Bayesian Decision-Making. - Ju He, Qihang Yu, Inkyu Shin, Xueqing Deng, Alan L. Yuille, Xiaohui Shen, Liang-Chieh Chen:
A Simple Video Segmenter by Tracking Objects Along Axial Trajectories. - Arkadiy Dushatskiy, Esther Julien, Leen Stougie, Leo van Iersel:
Solving the Tree Containment Problem Using Graph Neural Networks. - Christopher Beckham, Alexandre Piché, David Vázquez, Christopher Pal:
Exploring validation metrics for offline model-based optimisation with diffusion models. - Kostis Gourgoulias, Najah Ghalyan, Maxime Labonne, Yash Satsangi, Sean Moran, Joseph Sabelja:
Estimating class separability of text embeddings with persistent homology. - Hashem Ghanem, Samuel Vaiter, Nicolas Keriven:
Gradient Scarcity in Graph Learning with Bilevel Optimization. - Timm Hess, Eli Verwimp, Gido M. van de Ven, Tinne Tuytelaars:
Knowledge Accumulation in Continually Learned Representations and the Issue of Feature Forgetting. - Yuqing Qian, Ziyu Zheng, Prayag Tiwari, Yijie Ding, Quan Zou:
Multiple Kronecker RLS fusion-based link propagation for drug-side effect prediction. - Gijs de Jong, Macha J. Meijer, Derck W. E. Prinzhorn, Harold Ruiter:
Reproducibility study of FairAC. - Patrik Joslin Kenfack, Samira Ebrahimi Kahou, Ulrich Aïvodji:
A Survey on Fairness Without Demographics. - Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang:
Deep End-to-end Causal Inference. - Linjie Xu, Zhengyao Jiang, Jinyu Wang, Lei Song, Jiang Bian:
Mildly Constrained Evaluation Policy for Offline Reinforcement Learning. - Yinsong Wang, Shahin Shahrampour:
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality. - Lucas Ponticelli, Vincent Loos, Eren Kocadag, Kacper Bartosik:
Reproducibility study of "Robust Fair Clustering: A Novel Fairness Attack and Defense Framework". - Kotaro Yoshida, Hiroki Naganuma:
Towards Understanding Variants of Invariant Risk Minimization through the Lens of Calibration. - Lily H. Zhang, Rajesh Ranganath, Arya Tafvizi:
Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. - Akshay Kumar, Jarvis D. Haupt:
Directional Convergence Near Small Initializations and Saddles in Two-Homogeneous Neural Networks. - Shamik Kundu, Sanjay Das, Sayar Karmakar, Arnab Raha, Souvik Kundu, Yiorgos Makris, Kanad Basu:
Bit-by-Bit: Investigating the Vulnerabilities of Binary Neural Networks to Adversarial Bit Flipping. - Qi Yan, Zhengyang Liang, Yang Song, Renjie Liao, Lele Wang:
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation. - Samuel Stocksieker, Denys Pommeret, Arthur Charpentier:
Generalized Oversampling for Learning from Imbalanced datasets and Associated Theory: Application in Regression. - Wes Gurnee, Theo Horsley, Zifan Carl Guo, Tara Rezaei Kheirkhah, Qinyi Sun, Will Hathaway, Neel Nanda, Dimitris Bertsimas:
Universal Neurons in GPT2 Language Models. - Ryan Boustany:
On the numerical reliability of nonsmooth autodiff: a MaxPool case study. - Nicolò Cesa-Bianchi, Tommaso Cesari, Riccardo Della Vecchia:
Cooperative Online Learning with Feedback Graphs. - Rui-Jie Zhu, Qihang Zhao, Guoqi Li, Jason Eshraghian:
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks. - Nikhil Parthasarathy, Olivier J. Hénaff, Eero P. Simoncelli:
Layerwise complexity-matched learning yields an improved model of cortical area V2. - Cheng Wang, Jacek Golebiowski:
Towards Unbiased Calibration using Meta-Regularization. - Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer:
Training-free linear image inverses via flows. - Kai Bäuerle, Patrick Müller, Syed Muhammad Kazim, Ivo Ihrke, Margret Keuper:
Learning the essential in less than 2k additional weights - a simple approach to improve image classification stability under corruptions. - Ryuji Saiin, Tomoya Shirakawa, Sota Yoshihara, Yoshihide Sawada, Hiroyuki Kusumoto:
Spike Accumulation Forwarding for Effective Training of Spiking Neural Networks. - Masahiro Kato, Shinji Ito:
Best-of-Both-Worlds Linear Contextual Bandits. - Lucas Zoroddu, Pierre Humbert, Laurent Oudre:
Learning Network Granger causality using Graph Prior Knowledge. - Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh:
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization. - Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara:
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. - Xi Fang, Weijie Xu, Fiona Anting Tan, Ziqing Hu, Jiani Zhang, Yanjun Qi, Srinivasan H. Sengamedu, Christos Faloutsos:
Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey. - Dongyue Li, Kailai Chen, Predrag Radivojac, Hongyang R. Zhang:
Learning Tree-Structured Composition of Data Augmentation. - Da Yu, Sivakanth Gopi, Janardhan Kulkarni, Zinan Lin, Saurabh Naik, Tomasz Lukasz Religa, Jian Yin, Huishuai Zhang:
Selective Pre-training for Private Fine-tuning. - Daiqing Qi, Handong Zhao, Xiaowei Jia, Sheng Li:
Revealing an Overlooked Challenge in Class-Incremental Graph Learning. - Wouter Bant, Ádám Divák, Jasper Eppink, Floris Six Dijkstra:
On the Reproducibility of: "Learning Perturbations to Explain Time Series Predictions". - Daniel Gallo Fernández, Razvan-Andrei Matisan, Alejandro Monroy Muñoz, Janusz Partyka:
Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation". - Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, Tong Zhang:
On the Unreasonable Effectiveness of Federated Averaging with Heterogeneous Data. - Frédéric Chazal, Laure Ferraris, Pablo Groisman, Matthieu Jonckheere, Frédéric Pascal, Facundo Sapienza:
Choosing the parameter of the Fermat distance: navigating geometry and noise. - Satyapriya Krishna, Tessa Han, Alex Gu, Steven Wu, Shahin Jabbari, Himabindu Lakkaraju:
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective. - Mahdi Biparva, Raika Karimi, Faezeh Faez, Yingxue Zhang:
Todyformer: Towards Holistic Dynamic Graph Transformers with Structure-Aware Tokenization. - Camille Olivia Little, Debolina Halder Lina, Genevera I. Allen:
Fair Feature Importance Scores for Interpreting Decision Trees. - Etash Kumar Guha:
Solving Robust MDPs through No-Regret Dynamics. - Jin Huang, Xingjian Zhang, Qiaozhu Mei, Jiaqi Ma:
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why? - Sara Pyykölä, Klavdiya O. Bochenina, Laura Ruotsalainen:
Conciliator steering: Imposing user preference in multi-objective reinforcement learning. - Vaidotas Simkus, Michael U. Gutmann:
Improving Variational Autoencoder Estimation from Incomplete Data with Mixture Variational Families. - Shivank Garg, Manyana Tiwari:
Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images. - Jishnu Mukhoti, Yarin Gal, Philip Torr, Puneet K. Dokania:
Fine-tuning can cripple your foundation model; preserving features may be the solution. - Maxwell Horton, Sachin Mehta, Ali Farhadi, Mohammad Rastegari:
Bytes Are All You Need: Transformers Operating Directly On File Bytes. - Fahdi Kanavati, Lucy Katsnith, Masayuki Tsuneki:
σ-PCA: a building block for neural learning of identifiable linear transformations. - Markus Semmler, Miguel de Benito Delgado:
[Re] Classwise-Shapley values for data valuation. - Elisabeth Gassiat, Sylvain Le Corff:
Variational excess risk bound for general state space models. - Weiming Ren, Huan Yang, Ge Zhang, Cong Wei, Xinrun Du, Wenhao Huang, Wenhu Chen:
ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generation. - Han Wang, Aritra Mitra, Hamed Hassani, George J. Pappas, James Anderson:
Federated TD Learning with Linear Function Approximation under Environmental Heterogeneity. - Viet Duong, Qiong Wu, Zhengyi Zhou, Eric Zavesky, Wen-Ling Hsu, Han Zhao, Huajie Shao:
A General-Purpose Multi-Modal OOD Detection Framework. - Luca Butera, Andrea Cini, Alberto Ferrante, Cesare Alippi:
Object-Centric Relational Representations for Image Generation. - Giuseppina Carannante, Nidhal Bouaynaya, Ghulam Rasool, Lyudmila Mihaylova:
BaSIS-Net: From Point Estimate to Predictive Distribution in Neural Networks - A Bayesian Sequential Importance Sampling Framework. - Ruosen Li, Teerth Patel, Xinya Du:
PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations. - Oliver Bentham, Nathan Stringham, Ana Marasovic:
Chain-of-Thought Unfaithfulness as Disguised Accuracy. - Stefan Bluecher, Johanna Vielhaben, Nils Strodthoff:
Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks. - Amir Rahimi, Vanessa D'Amario, Moyuru Yamada, Kentaro Takemoto, Tomotake Sasaki, Xavier Boix:
D3: Data Diversity Design for Systematic Generalization in Visual Question Answering. - Kamyar Azizzadenesheli, William Lu, Anuran Makur, Qian Zhang:
Sparse Contextual CDF Regression. - Jaedong Hwang, Zhang-Wei Hong, Eric Chen, Akhilan Boopathy, Pulkit Agrawal, Ila R. Fiete:
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building. - Adam Ibrahim, Benjamin Thérien, Kshitij Gupta, Mats L. Richter, Quentin Gregory Anthony, Eugene Belilovsky, Timothée Lesort, Irina Rish:
Simple and Scalable Strategies to Continually Pre-train Large Language Models. - Weizhi Li, Prad Kadambi, Pouria Saidi, Karthikeyan Natesan Ramamurthy, Gautam Dasarathy, Visar Berisha:
Active Sequential Two-Sample Testing. - Ajinkya K. Mulay, Xiaojun Lin:
SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery. - Henrik Häggström, Pedro L. C. Rodrigues, Geoffroy Oudoumanessah, Florence Forbes, Umberto Picchini:
Fast, accurate and lightweight sequential simulation-based inference using Gaussian locally linear mappings. - Jinxuan Xu, Hong-You Chen, Wei-Lun Chao, Yuqian Zhang:
Jigsaw Game: Federated Clustering. - Till Speicher, Vedant Nanda, Krishna P. Gummadi:
Understanding the Role of Invariance in Transfer Learning. - Chuanyang Zheng, Haiming Wang, Enze Xie, Zhengying Liu, Jiankai Sun, Huajian Xin, Jianhao Shen, Zhenguo Li, Yu Li:
Lyra: Orchestrating Dual Correction in Automated Theorem Proving. - Sarah Ibrahimi, Mina Ghadimi Atigh, Nanne van Noord, Pascal Mettes, Marcel Worring:
Intriguing Properties of Hyperbolic Embeddings in Vision-Language Models. - Chenguo Lin, Xumeng Wen, Wei Cao, Congrui Huang, Jiang Bian, Stephen Lin, Zhirong Wu:
NuTime: Numerically Multi-Scaled Embedding for Large- Scale Time-Series Pretraining. - Ville Tanskanen, Chang Rajani, Perttu Hämäläinen, Christian Guckelsberger, Arto Klami:
Contextual Policies Enable Efficient and Interpretable Inverse Reinforcement Learning for Populations. - Qitong Wang, Georgios Kollias, Vasileios Kalantzis, Naoki Abe, Mohammed J. Zaki:
Directed Graph Transformers. - Eduardo Dadalto Câmara Gomes, Florence Alberge, Pierre Duhamel, Pablo Piantanida:
Combine and Conquer: A Meta-Analysis on Data Shift and Out-of-Distribution Detection. - Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi, Kaushik Roy:
Homogenizing Non-IID Datasets via In-Distribution Knowledge Distillation for Decentralized Learning. - Geonmo Gu, Sanghyuk Chun, Wonjae Kim, HeeJae Jun, Yoohoon Kang, Sangdoo Yun:
CompoDiff: Versatile Composed Image Retrieval With Latent Diffusion. - Sanket Rajan Gupte, Josiah Aklilu, Jeffrey J. Nirschl, Serena Yeung-Levy:
Revisiting Active Learning in the Era of Vision Foundation Models. - Omprakash Chakraborty, Aadarsh Sahoo, Rameswar Panda, Abir Das:
XPL: A Cross-Model framework for Semi-Supervised Prompt Learning in Vision-Language Models. - Mrinank Sharma, Tom Rainforth, Yee Whye Teh, Vincent Fortuin:
Incorporating Unlabelled Data into Bayesian Neural Networks. - Futoon M. Abushaqra, Hao Xue, Yongli Ren, Flora D. Salim:
SeqLink: A Robust Neural-ODE Architecture for Modelling Partially Observed Time Series. - Berkay Chakar, Amina Izbassar, Mina Janicijevic, Jakub Tomaszewski:
Reproducibility Study: Equal Improvability: A New Fairness Notion Considering the Long-Term Impact. - Kyra Ahrens, Hans Hergen Lehmann, Jae Hee Lee, Stefan Wermter:
Read Between the Layers: Leveraging Multi-Layer Representations for Rehearsal-Free Continual Learning with Pre-Trained Models. - Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang:
A Survey on Data Selection for Language Models. - Yassine Naji, Romaric Audigier, Aleksandr Setkov, Angelique Loesch, Michèle Gouiffès:
Masked multi-prediction for multi-aspect anomaly detection. - Hédi Hadiji, Sébastien Gerchinovitz, Jean-Michel Loubes, Gilles Stoltz:
Diversity-Preserving K-Armed Bandits, Revisited. - Chengshuai Shi, Ruida Zhou, Kun Yang, Cong Shen:
Harnessing the Power of Federated Learning in Federated Contextual Bandits. - Chen Li, Yixiao Ge, Dian Li, Ying Shan:
Vision-Language Instruction Tuning: A Review and Analysis. - Buck Shlegeris, Fabien Roger, Lawrence Chan, Euan McLean:
Language Models Are Better Than Humans at Next-token Prediction. - Tong Xie, Haoyu Li, Andrew Bai, Cho-Jui Hsieh:
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation. - Sokhna Diarra Mbacke, Omar Rivasplata:
A Note on the Convergence of Denoising Diffusion Probabilistic Models. - Francesco Quinzan, Cecilia Casolo, Krikamol Muandet, Yucen Luo, Niki Kilbertus:
Learning Counterfactually Invariant Predictors. - Pascal Mattia Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar:
Representation Learning Dynamics of Self-Supervised Models. - Adriano Fazzone, Yanhao Wang, Francesco Bonchi:
Fair Representation in Submodular Subset Selection: A Pareto Optimization Approach. - Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri:
XAudit : A Learning-Theoretic Look at Auditing with Explanations. - Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noé, Regina Barzilay, Tommi S. Jaakkola:
Improved motif-scaffolding with SE(3) flow matching. - Jaewoong Cho, Kartik Sreenivasan, Keon Lee, Kyunghoo Mun, Soheun Yi, Jeong-Gwan Lee, Anna Lee, Jy-yong Sohn, Dimitris Papailiopoulos, Kangwook Lee:
Mini-Batch Optimization of Contrastive Loss. - Junjie Yang, Matthieu Labeau, Florence d'Alché-Buc:
Exploiting Edge Features in Graph-based Learning with Fused Network Gromov-Wasserstein Distance. - Nandan Kumar Jha, Brandon Reagen:
DeepReShape: Redesigning Neural Networks for Efficient Private Inference. - Róisín Luo, James McDermott, Colm O'Riordan:
Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition. - Yannis Karmim, Elias Ramzi, Raphaël Fournier-S'niehotta, Nicolas Thome:
ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation. - Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek:
On the Importance of Uncertainty in Decision-Making with Large Language Models. - Weili Shi, Sheng Li:
Dual-windowed Vision Transformer with Angular Self- Attention. - Sangamesh Kodge, Gobinda Saha, Kaushik Roy:
Deep Unlearning: Fast and Efficient Gradient-free Class Forgetting. - Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov, Jesse C. Cresswell, Franziska Boenisch, Nicolas Papernot:
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness. - Takashi Takahashi:
A replica analysis of under-bagging. - Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho:
Neural Clamping: Joint Input Perturbation and Temperature Scaling for Neural Network Calibration. - Edo Cohen-Karlik, Eyal Rozenberg, Daniel Freedman:
Overcoming Order in Autoregressive Graph Generation for Molecule Generation. - Louis Mahon, Lei Sha, Thomas Lukasiewicz:
Correcting Flaws in Common Disentanglement Metrics. - Fabio Valerio Massoli, Christos Louizos, Arash Behboodi:
Variational Learning ISTA. - Hongru Zhao, Jinchao Xu:
Convergence Analysis and Trajectory Comparison of Gradient Descent for Overparameterized Deep Linear Networks. - Abhishek Sharma, Catherine Zeng, Sanjana Narayanan, Sonali Parbhoo, Roy H. Perlis, Finale Doshi-Velez:
Task-Relevant Feature Selection with Prediction Focused Mixture Models. - Jean V. Alves, Diogo Leitão, Sérgio M. Jesus, Marco O. P. Sampaio, Javier Liébana, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro:
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints. - Prayag Tiwari, Farid Saberi Movahed, Saeed Karami, Farshad Saberi-Movahed, Jens Lehmann, Sahar Vahdati:
A Self-Representation Learning Method for Unsupervised Feature Selection using Feature Space Basis. - Vladimír Boza:
Fast and Effective Weight Update for Pruned Large Language Models. - Zhiquan Tan, Zihao Wang, Yifan Zhang:
SEAL: Simultaneous Label Hierarchy Exploration And Learning. - Matthew Lau, Ismaïla Seck, Athanasios P. Meliopoulos, Wenke Lee, Eugène Ndiaye:
Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection. - Shizhuo Dylan Zhang, Curt Tigges, Zory Zhang, Stella Biderman, Maxim Raginsky, Talia Ringer:
Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion. - Alex Hernández-García, Nikita Saxena, Moksh Jain, Cheng-Hao Liu, Yoshua Bengio:
Multi-Fidelity Active Learning with GFlowNets. - Tongxin Yin, Xuwei Tan, Xueru Zhang, Mohammad Mahdi Khalili, Mingyan Liu:
Federated Learning with Reduced Information Leakage and Computation. - Gina Wong, Joshua Gleason, Rama Chellappa, Yoav Wald, Anqi Liu:
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift. - Subhabrata Dutta, Joykirat Singh, Soumen Chakrabarti, Tanmoy Chakraborty:
How to think step-by-step: A mechanistic understanding of chain-of-thought reasoning. - Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic:
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. - Frida Marie Viset, Anton Kullberg, Frederiek Wesel, Arno Solin:
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices. - Ori Katz, Ronen Talmon, Uri Shaham:
Supervised Domain Adaptation Based on Marginal and Conditional Distributions Alignment. - Klaus-Rudolf Kladny, Julius von Kügelgen, Bernhard Schölkopf, Michael Muehlebach:
Deep Backtracking Counterfactuals for Causally Compliant Explanations.
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