Approximating Continuous Functions by ReLU Nets of Minimal Width. - Boris Hanin; Mark Sellke (CoRR, 2017)

Stabilizing a system with an unbounded random gain using only a finite number of bits. - Victoria Kostina; Yuval Peres; Gireeja Ranade; Mark Sellke (CoRR, 2018)

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (CoRR, 2018)

First-Order Regret Analysis of Thompson Sampling. - Sébastien Bubeck; Mark Sellke (CoRR, 2019)

Vertex Sparsifiers for c-Edge Connectivity. - Yang P. Liu; Richard Peng; Mark Sellke (CoRR, 2019)

Competitively Chasing Convex Bodies. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SIAM J. Comput., 2019)

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

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Bo'az Klartag; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SODA, 2020)

Chasing Convex Bodies Optimally. - Mark Sellke (SODA, 2020)

Sample Complexity of Incentivized Exploration. - Mark Sellke; Aleksandrs Slivkins (CoRR, 2020)

Algorithmic pure states for the negative spherical perceptron. - Ahmed El Alaoui; Mark Sellke (CoRR, 2020)

Functions that Preserve Manhattan Distances. - Timothy Chu; Gary L. Miller; Shyam Narayanan; Mark Sellke (CoRR, 2020)

Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. - Sébastien Bubeck; Thomas Budzinski; Mark Sellke (COLT, 2021)

Metrical Service Systems with Transformations. - Sébastien Bubeck; Niv Buchbinder; Christian Coester; Mark Sellke (ITCS, 2021)

Online Multiserver Convex Chasing and Optimization. - Sébastien Bubeck; Yuval Rabani; Mark Sellke (SODA, 2021)

Vertex Sparsification for Edge Connectivity. - Parinya Chalermsook; Syamantak Das; Yunbum Kook; Bundit Laekhanukit; Yang P. Liu; Richard Peng; Mark Sellke; Daniel Vaz 001 (SODA, 2021)

Stabilizing a System With an Unbounded Random Gain Using Only Finitely Many Bits. - Victoria Kostina; Yuval Peres; Gireeja Ranade; Mark Sellke (IEEE Trans. Inf. Theory, 2021)

The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. - Allen Liu; Mark Sellke (COLT, 2022)

Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization. - Ahmed El Alaoui; Andrea Montanari; Mark Sellke (FOCS, 2022)

Tight Lipschitz Hardness for optimizing Mean Field Spin Glasses. - Brice Huang; Mark Sellke (FOCS, 2022)

Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. - Yining Chen; Elan Rosenfeld; Mark Sellke; Tengyu Ma 001; Andrej Risteski (NeurIPS, 2022)

Tight Bounds for State Tomography with Incoherent Measurements. - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (CoRR, 2022)

Exact Minimum Number of Bits to Stabilize a Linear System. - Victoria Kostina; Yuval Peres; Gireeja Ranade; Mark Sellke (IEEE Trans. Autom. Control., 2022)

When Does Adaptivity Help for Quantum State Learning? - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (FOCS, 2023)

Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting. - Ofer Grossman; Meghal Gupta; Mark Sellke (FOCS, 2023)

Incentivizing Exploration with Linear Contexts and Combinatorial Actions. - Mark Sellke (ICML, 2023)

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

Algorithmic Threshold for Multi-Species Spherical Spin Glasses. - Brice Huang; Mark Sellke (CoRR, 2023)

Asymptotically Optimal Pure Exploration for Infinite-Armed Bandits. - Evelyn Xiao-Yue Gong; Mark Sellke (CoRR, 2023)

The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. - Mark Sellke; Aleksandrs Slivkins (Oper. Res., 2023)

First-Order Bayesian Regret Analysis of Thompson Sampling. - Sébastien Bubeck; Mark Sellke (IEEE Trans. Inf. Theory, 2023)

A Universal Law of Robustness via Isoperimetry. - Sébastien Bubeck; Mark Sellke (J. ACM, 2023)

Local algorithms for maximum cut and minimum bisection on locally treelike regular graphs of large degree. - Ahmed El Alaoui; Andrea Montanari; Mark Sellke (Random Struct. Algorithms, 2023)

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

Metric Transforms and Low Rank Representations of Kernels for Fast Attention. - Timothy Chu; Josh Alman; Gary L. Miller; Shyam Narayanan; Mark Sellke; Zhao Song 002 (NeurIPS, 2024)

Improved Lower Bound for Frankl's Union-Closed Sets Conjecture. - Ryan Alweiss; Brice Huang; Mark Sellke (Electron. J. Comb., 2024)

On size-independent sample complexity of ReLU networks. - Mark Sellke (Inf. Process. Lett., 2024)

Strong Low Degree Hardness for Stable Local Optima in Spin Glasses. - Brice Huang; Mark Sellke (CoRR, 2025)

Tight Low Degree Hardness for Optimizing Pure Spherical Spin Glasses. - Mark Sellke (CoRR, 2025)

Strong Low Degree Hardness for the Number Partitioning Problem. - Rushil Mallarapu; Mark Sellke (CoRR, 2025)

Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts. - Benjamin Schiffer; Mark Sellke (CoRR, 2025)