Papers
The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data
Thomas Davies, Zhengchao Wan, Ruben J Sanchez-Garcia
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek et al.
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
Xingyu Xu, Yandi Shen, Yuejie Chi et al.
The Power of Uniform Sampling for k-Median
Lingxiao Huang, Shaofeng H.-C. Jiang, Jianing Lou
The Price of Differential Privacy under Continual Observation
Palak Jain, Sofya Raskhodnikova, Satchit Sivakumar et al.
The Regret of Exploration and the Control of Bad Episodes in Reinforcement Learning
Victor Boone, Bruno Gaujal
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
Borja Rodrı́guez Gálvez, Arno Blaas, Pau Rodriguez et al.
The Saddle-Point Method in Differential Privacy
Wael Alghamdi, Juan Felipe Gomez, Shahab Asoodeh et al.
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak Kiani, Randall Balestriero et al.
The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation
Mark Rowland, Yunhao Tang, Clare Lyle et al.
The Statistical Scope of Multicalibration
Georgy Noarov, Aaron Roth
The Test of Tests: A Framework for Differentially Private Hypothesis Testing
Zeki Kazan, Kaiyan Shi, Adam Groce et al.
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Sarah Rathnam, Sonali Parbhoo, Weiwei Pan et al.
The Unreasonable Effectiveness of Few-shot Learning for Machine Translation
Xavier Garcia, Yamini Bansal, Colin Cherry et al.
The Value of Out-of-Distribution Data
Ashwin De Silva, Rahul Ramesh, Carey Priebe et al.
The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
Anirudh Vemula, Yuda Song, Aarti Singh et al.
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Tianjun Zhang, Fangchen Liu, Justin Wong et al.
Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits
Sunrit Chakraborty, Saptarshi Roy, Ambuj Tewari
Thompson Sampling with Diffusion Generative Prior
Yu-Guan Hsieh, Shiva Kasiviswanathan, Branislav Kveton et al.
Thompson Sampling with Less Exploration is Fast and Optimal
Tianyuan Jin, Xianglin Yang, Xiaokui Xiao et al.
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Emirhan Kurtuluş, Zichao Li, Yann Dauphin et al.
Tight and fast generalization error bound of graph embedding in metric space
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki et al.
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations
Hong-Ming Chiu, Richard Y. Zhang
Tight Data Access Bounds for Private Top-$k$ Selection
Hao Wu, Olga Ohrimenko, Anthony Wirth