| 2021 | Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions. | Yihan Wu, Aleksandar Bojchevski, Aleksei Kuvshinov, Stephan Gnnemann |
| 2021 | Adaptive wavelet pooling for convolutional neural networks. | Moritz Wolter, Jochen Garcke |
| 2021 | Sparse Algorithms for Markovian Gaussian Processes. | William J. Wilkinson, Arno Solin, Vincent Adam |
| 2021 | Moment-Based Variational Inference for Stochastic Differential Equations. | Christian Wildner, Heinz Koeppl |
| 2021 | Foundations of Bayesian Learning from Synthetic Data. | Harrison Wilde, Jack Jewson, Sebastian J. Vollmer, Chris C. Holmes |
| 2021 | Bayesian Inference with Certifiable Adversarial Robustness. | Matthew Wicker, Luca Laurenti, Andrea Patane, Zhuotong Chen, Zheng Zhang, Marta Kwiatkowska |
| 2021 | Inference in Stochastic Epidemic Models via Multinomial Approximations. | Nick Whiteley, Lorenzo Rimella |
| 2021 | Algorithms for Fairness in Sequential Decision Making. | Min Wen, Osbert Bastani, Ufuk Topcu |
| 2021 | Goodness-of-Fit Test for Mismatched Self-Exciting Processes. | Song Wei, Shixiang Zhu, Minghe Zhang, Yao Xie |
| 2021 | Direct Loss Minimization for Sparse Gaussian Processes. | Yadi Wei, Rishit Sheth, Roni Khardon |
| 2021 | Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation. | Chen-Yu Wei, Mehdi Jafarnia-Jahromi, Haipeng Luo, Rahul Jain |
| 2021 | Sample Elicitation. | Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang |
| 2021 | Graphical Normalizing Flows. | Antoine Wehenkel, Gilles Louppe |
| 2021 | Latent Derivative Bayesian Last Layer Networks. | Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters |
| 2021 | Multitask Bandit Learning Through Heterogeneous Feedback Aggregation. | Zhi Wang, Chicheng Zhang, Manish Kumar Singh, Laurel D. Riek, Kamalika Chaudhuri |
| 2021 | A comparative study on sampling with replacement vs Poisson sampling in optimal subsampling. | HaiYing Wang, Jiahui Zou |
| 2021 | Multi-Fidelity High-Order Gaussian Processes for Physical Simulation. | Zheng Wang, Wei W. Xing, Robert Michael Kirby, Shandian Zhe |
| 2021 | Shapley Flow: A Graph-based Approach to Interpreting Model Predictions. | Jiaxuan Wang, Jenna Wiens, Scott M. Lundberg |
| 2021 | Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations? | Chaoqi Wang, Shengyang Sun, Roger B. Grosse |
| 2021 | The Multiple Instance Learning Gaussian Process Probit Model. | Fulton Wang, Ali Pinar |
| 2021 | Maximal Couplings of the Metropolis-Hastings Algorithm. | Guanyang Wang, John O'Leary, Pierre Jacob |
| 2021 | The Sample Complexity of Meta Sparse Regression. | Zhanyu Wang, Jean Honorio |
| 2021 | Experimental Design for Regret Minimization in Linear Bandits. | Andrew Wagenmaker, Julian Katz-Samuels, Kevin Jamieson |
| 2021 | Causal Modeling with Stochastic Confounders. | Thanh Vinh Vo, Pengfei Wei, Wicher Bergsma, Tze-Yun Leong |
| 2021 | Minimax Model Learning. | Cameron Voloshin, Nan Jiang, Yisong Yue |