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ORCIDMark Sellke
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- affiliation: Stanford University, Department of Mathematics, USA
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2020 – today
- 2024
[j8]Ryan Alweiss, Brice Huang, Mark Sellke:
Improved Lower Bound for Frankl's Union-Closed Sets Conjecture. Electron. J. Comb. 31(3) (2024)- [j7]Mark Sellke
:
On size-independent sample complexity of ReLU networks. Inf. Process. Lett. 186: 106482 (2024) - [c22]Tanishq Kumar, Kevin Luo, Mark Sellke:
No Free Prune: Information-Theoretic Barriers to Pruning at Initialization. ICML 2024 - [i29]Tanishq Kumar, Kevin Luo, Mark Sellke:
No Free Prune: Information-Theoretic Barriers to Pruning at Initialization. CoRR abs/2402.01089 (2024) - 2023
- [j6]Mark Sellke, Aleksandrs Slivkins:
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. Oper. Res. 71(5): 1706-1732 (2023) - [j5]Sébastien Bubeck, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. J. ACM 70(2): 10:1-10:18 (2023) - [j4]Ahmed El Alaoui, Andrea Montanari, Mark Sellke:
Local algorithms for maximum cut and minimum bisection on locally treelike regular graphs of large degree. Random Struct. Algorithms 63(3): 689-715 (2023) - [j3]Sébastien Bubeck, Mark Sellke:
First-Order Bayesian Regret Analysis of Thompson Sampling. IEEE Trans. Inf. Theory 69(3): 1795-1823 (2023) - [c21]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
When Does Adaptivity Help for Quantum State Learning? FOCS 2023: 391-404 - [c20]Ofer Grossman, Meghal Gupta, Mark Sellke:
Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting. FOCS 2023: 1496-1504 - [c19]Mark Sellke:
Incentivizing Exploration with Linear Contexts and Combinatorial Actions. ICML 2023: 30570-30583 - [c18]Evelyn Xiao-Yue Gong, Mark Sellke:
Asymptotically Optimal Quantile Pure Exploration for Infinite-Armed Bandits. NeurIPS 2023 - [i28]Brice Huang, Mark Sellke:
Algorithmic Threshold for Multi-Species Spherical Spin Glasses. CoRR abs/2303.12172 (2023) - [i27]Ofer Grossman, Meghal Gupta, Mark Sellke:
Tight Space Lower Bound for Pseudo-Deterministic Approximate Counting. CoRR abs/2304.01438 (2023) - [i26]Mark Sellke:
Incentivizing Exploration with Linear Contexts and Combinatorial Actions. CoRR abs/2306.01990 (2023) - [i25]Mark Sellke:
On Size-Independent Sample Complexity of ReLU Networks. CoRR abs/2306.01992 (2023) - [i24]Evelyn Xiao-Yue Gong, Mark Sellke:
Asymptotically Optimal Pure Exploration for Infinite-Armed Bandits. CoRR abs/2306.01995 (2023) - 2022
- [j2]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Exact Minimum Number of Bits to Stabilize a Linear System. IEEE Trans. Autom. Control. 67(10): 5548-5554 (2022) - [c17]Allen Liu, Mark Sellke:
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. COLT 2022: 3094 - [c16]Brice Huang, Mark Sellke:
Tight Lipschitz Hardness for optimizing Mean Field Spin Glasses. FOCS 2022: 312-322 - [c15]Ahmed El Alaoui, Andrea Montanari, Mark Sellke:
Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization. FOCS 2022: 323-334 - [c14]Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski:
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. NeurIPS 2022 - [i23]Allen Liu, Mark Sellke:
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. CoRR abs/2202.09653 (2022) - [i22]Ahmed El Alaoui, Andrea Montanari, Mark Sellke:
Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic stochastic localization. CoRR abs/2203.05093 (2022) - [i21]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
Tight Bounds for State Tomography with Incoherent Measurements. CoRR abs/2206.05265 (2022) - 2021
- [j1]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Stabilizing a System With an Unbounded Random Gain Using Only Finitely Many Bits. IEEE Trans. Inf. Theory 67(4): 2554-2561 (2021) - [c13]Sébastien Bubeck, Thomas Budzinski, Mark Sellke:
Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. COLT 2021: 821-822 - [c12]Sébastien Bubeck, Niv Buchbinder, Christian Coester, Mark Sellke:
Metrical Service Systems with Transformations. ITCS 2021: 21:1-21:20 - [c11]Sébastien Bubeck, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. NeurIPS 2021: 28811-28822 - [c10]Mark Sellke, Aleksandrs Slivkins:
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity. EC 2021: 795-796 - [c9]Parinya Chalermsook, Syamantak Das, Yunbum Kook, Bundit Laekhanukit, Yang P. Liu, Richard Peng, Mark Sellke, Daniel Vaz:
Vertex Sparsification for Edge Connectivity. SODA 2021: 1206-1225 - [c8]Sébastien Bubeck, Yuval Rabani, Mark Sellke:
Online Multiserver Convex Chasing and Optimization. SODA 2021: 2093-2104 - [i20]Sébastien Bubeck, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. CoRR abs/2105.12806 (2021) - [i19]Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski:
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments. CoRR abs/2106.09913 (2021) - [i18]Brice Huang, Mark Sellke:
Tight Lipschitz Hardness for Optimizing Mean Field Spin Glasses. CoRR abs/2110.07847 (2021) - [i17]Ahmed El Alaoui, Andrea Montanari, Mark Sellke:
Local algorithms for Maximum Cut and Minimum Bisection on locally treelike regular graphs of large degree. CoRR abs/2111.06813 (2021) - 2020
- [c7]Sébastien Bubeck, Mark Sellke:
First-Order Bayesian Regret Analysis of Thompson Sampling. ALT 2020: 196-233 - [c6]Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke:
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without. COLT 2020: 961-987 - [c5]Sébastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Chasing Nested Convex Bodies Nearly Optimally. SODA 2020: 1496-1508 - [c4]Mark Sellke:
Chasing Convex Bodies Optimally. SODA 2020: 1509-1518 - [i16]Mark Sellke, Aleksandrs Slivkins:
Sample Complexity of Incentivized Exploration. CoRR abs/2002.00558 (2020) - [i15]Sébastien Bubeck, Yuval Rabani, Mark Sellke:
Online Multiserver Convex Chasing and Optimization. CoRR abs/2004.07346 (2020) - [i14]Parinya Chalermsook, Syamantak Das, Bundit Laekhanukit, Yunbum Kook, Yang P. Liu, Richard Peng, Mark Sellke, Daniel Vaz:
Vertex Sparsification for Edge Connectivity. CoRR abs/2007.07862 (2020) - [i13]Sébastien Bubeck, Niv Buchbinder, Christian Coester, Mark Sellke:
Metrical Service Systems with Transformations. CoRR abs/2009.08266 (2020) - [i12]Ahmed El Alaoui, Mark Sellke:
Algorithmic pure states for the negative spherical perceptron. CoRR abs/2010.15811 (2020) - [i11]Sébastien Bubeck, Thomas Budzinski, Mark Sellke:
Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. CoRR abs/2011.03896 (2020) - [i10]Timothy Chu, Gary L. Miller, Shyam Narayanan, Mark Sellke:
Functions that Preserve Manhattan Distances. CoRR abs/2011.11503 (2020)
2010 – 2019
- 2019
- [c3]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Competitively chasing convex bodies. STOC 2019: 861-868 - [i9]Sébastien Bubeck, Mark Sellke:
First-Order Regret Analysis of Thompson Sampling. CoRR abs/1902.00681 (2019) - [i8]Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke:
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without. CoRR abs/1904.12233 (2019) - [i7]Mark Sellke:
Chasing Convex Bodies Optimally. CoRR abs/1905.11968 (2019) - [i6]Yang P. Liu, Richard Peng, Mark Sellke:
Vertex Sparsifiers for c-Edge Connectivity. CoRR abs/1910.10359 (2019) - 2018
- [c2]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Exact minimum number of bits to stabilize a linear system. CDC 2018: 453-458 - [c1]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Stabilizing a System with an Unbounded Random Gain Using Only Finitely Many Bits. ISIT 2018: 2256-2260 - [i5]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Stabilizing a system with an unbounded random gain using only a finite number of bits. CoRR abs/1805.05535 (2018) - [i4]Victoria Kostina, Yuval Peres, Gireeja Ranade, Mark Sellke:
Exact minimum number of bits to stabilize a linear system. CoRR abs/1807.07686 (2018) - [i3]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Competitively Chasing Convex Bodies. CoRR abs/1811.00887 (2018) - [i2]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Chasing Nested Convex Bodies Nearly Optimally. CoRR abs/1811.00999 (2018) - 2017
- [i1]Boris Hanin, Mark Sellke:
Approximating Continuous Functions by ReLU Nets of Minimal Width. CoRR abs/1710.11278 (2017)
Coauthor Index
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last updated on 2024-10-23 20:35 CEST by the dblp team
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