Sellke, Mark

11 publications

NeurIPS 2025 Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts Benjamin Schiffer, Mark Sellke

NeurIPS 2024 Metric Transforms and Low Rank Representations of Kernels for Fast Attention Timothy Chu, Josh Alman, Gary Miller, Shyam Narayanan, Mark Sellke, Zhao Song

ICML 2024 No Free Prune: Information-Theoretic Barriers to Pruning at Initialization Tanishq Kumar, Kevin Luo, Mark Sellke

NeurIPS 2023 Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits Evelyn Xiao-Yue Gong, Mark Sellke

ICML 2023 Incentivizing Exploration with Linear Contexts and Combinatorial Actions Mark Sellke

NeurIPS 2022 Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski

COLT 2022 The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with No Communication Allen X Liu, Mark Sellke

NeurIPS 2021 A Universal Law of Robustness via Isoperimetry Sebastien Bubeck, Mark Sellke

COLT 2021 Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret with Neither Communication nor Collisions Sebastien Bubeck, Thomas Budzinski, Mark Sellke

ALT 2020 First-Order Bayesian Regret Analysis of Thompson Sampling Sébastien Bubeck, Mark Sellke

COLT 2020 Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate with Collision Information, Sublinear Without Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke