| 2024 | Preface. | |
| 2024 | Functional Wasserstein Bridge Inference for Bayesian Deep Learning. | Mengjing Wu, Junyu Xuan, Jie Lu |
| 2024 | Pix2Code: Learning to Compose Neural Visual Concepts as Programs. | Antonia Wst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting |
| 2024 | RE-SORT: Removing Spurious Correlation in Multilevel Interaction for CTR Prediction. | Songli Wu, Liang Du, Jiaqi Yang, Yuai Wang, De-Chuan Zhan, Shuang Zhao, Zixun Sun |
| 2024 | Understanding Pathologies of Deep Heteroskedastic Regression. | Eliot Wong-Toi, Alex Boyd, Vincent Fortuin, Stephan Mandt |
| 2024 | GCVR: Reconstruction from Cross-View Enable Sufficient and Robust Graph Contrastive Learning. | Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Chuxu Zhang, Yanfang Ye |
| 2024 | Hidden Population Estimation with Indirect Inference and Auxiliary Information. | Justin Weltz, Eric Laber, Alexander Volfovsky |
| 2024 | Robust Entropy Search for Safe Efficient Bayesian Optimization. | Dorina Weichert, Alexander Kister, Sebastian Houben, Patrick Link, Gunar Ernis |
| 2024 | Bounding causal effects with leaky instruments. | David S. Watson, Jordan Penn, Lee M. Gunderson, Gecia Bravo Hermsdorff, Afsaneh Mastouri, Ricardo Silva |
| 2024 | Stein Random Feature Regression. | Houston Warren, Rafael Oliveira, Fabio T. Ramos |
| 2024 | Model-Free Robust Reinforcement Learning with Sample Complexity Analysis. | Yudan Wang, Shaofeng Zou, Yue Wang |
| 2024 | Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning. | Xiao Wang, Jia Wang, Tong Zhao, Yijie Wang, Nan Zhang, Yong Zang, Sha Cao, Chi Zhang |
| 2024 | AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop. | Jing Wang, Yunfei Teng, Anna Choromanska |
| 2024 | Two Facets of SDE Under an Information-Theoretic Lens: Generalization of SGD via Training Trajectories and via Terminal States. | Ziqiao Wang, Yongyi Mao |
| 2024 | Pure Exploration in Asynchronous Federated Bandits. | Zichen Wang, Chuanhao Li, Chenyu Song, Lianghui Wang, Quanquan Gu, Huazheng Wang |
| 2024 | Beyond Dirichlet-based Models: When Bayesian Neural Networks Meet Evidential Deep Learning. | Hanjing Wang, Qiang Ji |
| 2024 | Group Fairness in Predict-Then-Optimize Settings for Restless Bandits. | Shresth Verma, Yunfan Zhao, Sanket Shah, Niclas Boehmer, Aparna Taneja, Milind Tambe |
| 2024 | Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks. | Shyam Venkatasubramanian, Ahmed Aloui, Vahid Tarokh |
| 2024 | Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs. | Filippo Valdettaro, Aldo Faisal |
| 2024 | Bayesian Pseudo-Coresets via Contrastive Divergence. | Piyush Tiwary, Kumar Shubham, Vivek Kashyap, Prathosh A. P. |
| 2024 | Fast Reliability Estimation for Neural Networks with Adversarial Attack-Driven Importance Sampling. | Karim Tit, Teddy Furon |
| 2024 | Localised Natural Causal Learning Algorithms for Weak Consistency Conditions. | Kai Z. Teh, Kayvan Sadeghi, Terry Soo |
| 2024 | A Homogenization Approach for Gradient-Dominated Stochastic Optimization. | Jiyuan Tan, Chenyu Xue, Chuwen Zhang, Qi Deng, Dongdong Ge, Yinyu Ye |
| 2024 | Multi-layer random features and the approximation power of neural networks. | Rustem Takhanov |
| 2024 | Computing Low-Entropy Couplings for Large-Support Distributions. | Samuel Sokota, Dylan Sam, Christian Schrder de Witt, Spencer Compton, Jakob N. Foerster, J. Zico Kolter |