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Noah Golowich
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2020 – today
- 2024
- [j5]Noah Golowich, Ankur Moitra:
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation. RLJ 1: 302-341 (2024) - [c35]Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin:
Near-Optimal Learning and Planning in Separated Latent MDPs. COLT 2024: 995-1067 - [c34]Constantinos Daskalakis, Noah Golowich:
Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? COLT 2024: 1263-1307 - [c33]Noah Golowich, Ankur Moitra:
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. COLT 2024: 1939-1981 - [c32]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. FOCS 2024: 1953-1967 - [c31]Constantinos Daskalakis, Noah Golowich, Nika Haghtalab, Abhishek Shetty:
Smooth Nash Equilibria: Algorithms and Complexity. ITCS 2024: 37:1-37:22 - [c30]Noah Golowich
, Ankur Moitra
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. STOC 2024: 183-193 - [c29]Yuval Dagan
From External to Swap Regret 2.0: An Efficient Reduction for Large Action Spaces. STOC 2024: 1216-1222 - [i44]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. CoRR abs/2404.03774 (2024) - [i43]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
On Learning Parities with Dependent Noise. CoRR abs/2404.11325 (2024) - [i42]Noah Golowich, Elad Hazan, Zhou Lu, Dhruv Rohatgi, Y. Jennifer Sun:
Online Control in Population Dynamics. CoRR abs/2406.01799 (2024) - [i41]Noah Golowich, Ankur Moitra:
Edit Distance Robust Watermarks for Language Models. CoRR abs/2406.02633 (2024) - [i40]Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin:
Near-Optimal Learning and Planning in Separated Latent MDPs. CoRR abs/2406.07920 (2024) - [i39]Noah Golowich, Ankur Moitra:
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. CoRR abs/2406.11640 (2024) - [i38]Constantinos Daskalakis, Noah Golowich:
Is Efficient PAC Learning Possible with an Oracle That Responds 'Yes' or 'No'? CoRR abs/2406.11667 (2024) - [i37]Noah Golowich, Ankur Moitra:
The Role of Inherent Bellman Error in Offline Reinforcement Learning with Linear Function Approximation. CoRR abs/2406.11686 (2024) - [i36]Constantinos Daskalakis, Gabriele Farina, Noah Golowich, Tuomas Sandholm, Brian Hu Zhang:
A Lower Bound on Swap Regret in Extensive-Form Games. CoRR abs/2406.13116 (2024) - [i35]Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich, Robert Kleinberg, Princewill Okoroafor:
Improved bounds for calibration via stronger sign preservation games. CoRR abs/2406.13668 (2024) - [i34]Noah Golowich, Ankur Moitra:
Edit Distance Robust Watermarks for Language Models. IACR Cryptol. ePrint Arch. 2024: 898 (2024) - 2023
- [c28]Dean P. Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin:
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. COLT 2023: 2678-2792 - [c27]Dylan J. Foster, Noah Golowich, Yanjun Han:
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. COLT 2023: 3969-4043 - [c26]Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang:
The Complexity of Markov Equilibrium in Stochastic Games. COLT 2023: 4180-4234 - [c25]Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games. COLT 2023: 5146-5198 - [c24]Dylan J. Foster, Noah Golowich, Sham M. Kakade:
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. ICML 2023: 10188-10221 - [c23]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. NeurIPS 2023 - [c22]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning and Learning in Partially Observable Systems via Filter Stability. STOC 2023: 349-362 - [i33]Dylan J. Foster, Noah Golowich, Yanjun Han:
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. CoRR abs/2301.08215 (2023) - [i32]Dylan J. Foster, Noah Golowich, Sham M. Kakade:
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. CoRR abs/2303.12287 (2023) - [i31]Dylan J. Foster, Dean P. Foster, Noah Golowich, Alexander Rakhlin:
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. CoRR abs/2305.00684 (2023) - [i30]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. CoRR abs/2309.09457 (2023) - [i29]Constantinos Daskalakis, Noah Golowich, Nika Haghtalab, Abhishek Shetty:
Smooth Nash Equilibria: Algorithms and Complexity. CoRR abs/2309.12226 (2023) - [i28]Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich:
From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces. CoRR abs/2310.19786 (2023) - 2022
- [c21]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. COLT 2022: 1716-1786 - [c20]Noah Golowich, Ankur Moitra:
Can Q-learning be Improved with Advice? COLT 2022: 4548-4619 - [c19]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. NeurIPS 2022 - [c18]Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Noah Golowich, Tuomas Sandholm:
Near-optimal no-regret learning for correlated equilibria in multi-player general-sum games. STOC 2022: 736-749 - [c17]Constantinos Daskalakis, Noah Golowich:
Fast rates for nonparametric online learning: from realizability to learning in games. STOC 2022: 846-859 - [i27]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Planning in Observable POMDPs in Quasipolynomial Time. CoRR abs/2201.04735 (2022) - [i26]Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin:
Smoothed Online Learning is as Easy as Statistical Learning. CoRR abs/2202.04690 (2022) - [i25]Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang:
The Complexity of Markov Equilibrium in Stochastic Games. CoRR abs/2204.03991 (2022) - [i24]Noah Golowich, Ankur Moitra, Dhruv Rohatgi:
Learning in Observable POMDPs, without Computationally Intractable Oracles. CoRR abs/2206.03446 (2022) - [i23]Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis, Manolis Zampetakis
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games. CoRR abs/2210.09769 (2022) - [i22]Dylan J. Foster, Noah Golowich, Jian Qian, Alexander Rakhlin, Ayush Sekhari:
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient. CoRR abs/2211.14250 (2022) - 2021
- [c16]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure b ounds for the Littlestone and threshold dimensions. ALT 2021: 686-696 - [c15]Noah Golowich:
Differentially Private Nonparametric Regression Under a Growth Condition. COLT 2021: 2149-2192 - [c14]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh
On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. EUROCRYPT (3) 2021: 463-488 - [c13]Noah Golowich, Roi Livni:
Littlestone Classes are Privately Online Learnable. NeurIPS 2021: 11462-11473 - [c12]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
Deep Learning with Label Differential Privacy. NeurIPS 2021: 27131-27145 - [c11]Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich:
Near-Optimal No-Regret Learning in General Games. NeurIPS 2021: 27604-27616 - [c10]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Sample-efficient proper PAC learning with approximate differential privacy. STOC 2021: 183-196 - [i21]Constantinos Daskalakis, Dylan J. Foster, Noah Golowich:
Independent Policy Gradient Methods for Competitive Reinforcement Learning. CoRR abs/2101.04233 (2021) - [i20]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Chiyuan Zhang:
On Deep Learning with Label Differential Privacy. CoRR abs/2102.06062 (2021) - [i19]Noah Golowich, Roi Livni:
Littlestone Classes are Privately Online Learnable. CoRR abs/2106.13513 (2021) - [i18]Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich:
Near-Optimal No-Regret Learning in General Games. CoRR abs/2108.06924 (2021) - [i17]Noah Golowich, Ankur Moitra:
Can Q-Learning be Improved with Advice? CoRR abs/2110.13052 (2021) - [i16]Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Noah Golowich, Tuomas Sandholm:
Near-Optimal No-Regret Learning for Correlated Equilibria in Multi-Player General-Sum Games. CoRR abs/2111.06008 (2021) - [i15]Constantinos Daskalakis, Noah Golowich:
Fast Rates for Nonparametric Online Learning: From Realizability to Learning in Games. CoRR abs/2111.08911 (2021) - [i14]Noah Golowich:
Differentially Private Nonparametric Regression Under a Growth Condition. CoRR abs/2111.12786 (2021) - 2020
- [c9]Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman E. Ozdaglar:
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems. COLT 2020: 1758-1784 - [c8]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. ITC 2020: 15:1-15:23 - [c7]Constantinos Daskalakis, Dylan J. Foster, Noah Golowich:
Independent Policy Gradient Methods for Competitive Reinforcement Learning. NeurIPS 2020 - [c6]Noah Golowich, Sarath Pattathil, Constantinos Daskalakis:
Tight last-iterate convergence rates for no-regret learning in multi-player games. NeurIPS 2020 - [c5]Noah Golowich, Madhu Sudan:
Round Complexity of Common Randomness Generation: The Amortized Setting. SODA 2020: 1076-1095 - [i13]Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman E. Ozdaglar:
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems. CoRR abs/2002.00057 (2020) - [i12]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker:
Pure Differentially Private Summation from Anonymous Messages. CoRR abs/2002.01919 (2020) - [i11]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Near-tight closure bounds for Littlestone and threshold dimensions. CoRR abs/2007.03668 (2020) - [i10]Noah Golowich, Sarath Pattathil, Constantinos Daskalakis:
Tight last-iterate convergence rates for no-regret learning in multi-player games. CoRR abs/2010.13724 (2020) - [i9]Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi:
Sample-efficient proper PAC learning with approximate differential privacy. CoRR abs/2012.03893 (2020)
2010 – 2019
- 2019
- [c4]Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu:
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. ICLR (Poster) 2019 - [c3]Madhu Sudan, Badih Ghazi, Noah Golowich, Mitali Bafna:
Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation. SODA 2019: 1861-1871 - [i8]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
Private Heavy Hitters and Range Queries in the Shuffled Model. CoRR abs/1908.11358 (2019) - [i7]Noah Golowich, Madhu Sudan:
Round Complexity of Common Randomness Generation: The Amortized Setting. CoRR abs/1909.00323 (2019) - [i6]Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker:
On the Power of Multiple Anonymous Messages. IACR Cryptol. ePrint Arch. 2019: 1382 (2019) - 2018
- [j4]Noah Golowich:
Coloring Chains for Compression with Uncertain Priors. Electron. J. Comb. 25(4): 4 (2018) - [c2]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. COLT 2018: 297-299 - [c1]Noah Golowich, Harikrishna Narasimhan, David C. Parkes:
Deep Learning for Multi-Facility Location Mechanism Design. IJCAI 2018: 261-267 - [i5]Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso A. Poggio:
Theory of Deep Learning IIb: Optimization Properties of SGD. CoRR abs/1801.02254 (2018) - [i4]Mitali Bafna, Badih Ghazi, Noah Golowich, Madhu Sudan:
Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation. CoRR abs/1808.08907 (2018) - [i3]Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu:
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. CoRR abs/1810.02281 (2018) - [i2]Mitali Bafna, Badih Ghazi, Noah Golowich, Madhu Sudan:
Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation. Electron. Colloquium Comput. Complex. TR18 (2018) - 2017
- [i1]Noah Golowich, Alexander Rakhlin, Ohad Shamir:
Size-Independent Sample Complexity of Neural Networks. CoRR abs/1712.06541 (2017) - 2016
- [j3]Noah Golowich:
The m-degenerate chromatic number of a digraph. Discret. Math. 339(6): 1734-1743 (2016) - 2015
- [j2]Noah Golowich, David Rolnick
Acyclic Subgraphs of Planar Digraphs. Electron. J. Comb. 22(3): 3 (2015) - 2014
- [j1]Noah Golowich:
Resolving a Conjecture on Degree of Regularity of Linear Homogeneous Equations. Electron. J. Comb. 21(3): 3 (2014)
Coauthor Index
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last updated on 2024-12-10 20:50 CET by the dblp team
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