Papers
Thompson Sampling with a Mixture Prior
Joey Hong, Branislav Kveton, Manzil Zaheer et al.
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu et al.
Tight bounds for minimum $\ell_1$-norm interpolation of noisy data
Guillaume Wang, Konstantin Donhauser, Fanny Yang
Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation
Shuai Xiao, Zaifan Jiang, Shuang Yang
Top K Ranking for Multi-Armed Bandit with Noisy Evaluations
Evrard Garcelon, Vashist Avadhanula, Alessandro Lazaric et al.
Towards an Understanding of Default Policies in Multitask Policy Optimization
Ted Moskovitz, Michael Arbel, Jack Parker-Holder et al.
Towards Federated Bayesian Network Structure Learning with Continuous Optimization
Ignavier Ng, Kun Zhang
Towards Return Parity in Markov Decision Processes
Jianfeng Chi, Jian Shen, Xinyi Dai et al.
Towards Statistical and Computational Complexities of Polyak Step Size Gradient Descent
Tongzheng Ren, Fuheng Cui, Alexia Atsidakou et al.
Towards Understanding Biased Client Selection in Federated Learning
Yae Jee Cho, Jianyu Wang, Gauri Joshi
Transductive Robust Learning Guarantees
Omar Montasser, Steve Hanneke, Nathan Srebro
Transfer Learning with Gaussian Processes for Bayesian Optimization
Petru Tighineanu, Kathrin Skubch, Paul Baireuther et al.
Triangular Flows for Generative Modeling: Statistical Consistency, Smoothness Classes, and Fast Rates
Nicholas J. Irons, Meyer Scetbon, Soumik Pal et al.
Triple-Q: A Model-Free Algorithm for Constrained Reinforcement Learning with Sublinear Regret and Zero Constraint Violation
Honghao Wei, Xin Liu, Lei Ying
Tuning-Free Generalized Hamiltonian Monte Carlo
Matthew D. Hoffman, Pavel Sountsov
Two-Sample Test with Kernel Projected Wasserstein Distance
Jie Wang, Rui Gao, Yao Xie
Two-way Sparse Network Inference for Count Data
Sijia Li, Martı́n López-Garcı́a, Neil D. Lawrence et al.
Uncertainty Quantification for Bayesian Optimization
Rui Tuo, Wenjia Wang
Uncertainty Quantification for Low-Rank Matrix Completion with Heterogeneous and Sub-Exponential Noise
Vivek Farias, Andrew A. Li, Tianyi Peng
Unlabeled Data Help: Minimax Analysis and Adversarial Robustness
Yue Xing, Qifan Song, Guang Cheng
Using time-series privileged information for provably efficient learning of prediction models
Rickard K.A. Karlsson, Martin Willbo, Zeshan M. Hussain et al.
Vanishing Curvature in Randomly Initialized Deep ReLU Networks
Antonio Orvieto, Jonas Kohler, Dario Pavllo et al.
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Guillaume G. Martinet, Alexander Strzalkowski, Barbara Engelhardt