Noah Golowich

According to our database1, Noah Golowich authored at least 42 papers between 2014 and 2024.

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Bibliography

2024
Smooth Nash Equilibria: Algorithms and Complexity.
Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

2023
From External to Swap Regret 2.0: An Efficient Reduction and Oblivious Adversary for Large Action Spaces.
CoRR, 2023

Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles.
CoRR, 2023

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring.
CoRR, 2023

Planning and Learning in Partially Observable Systems via Filter Stability.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Model-Free Reinforcement Learning with the Decision-Estimation Coefficient.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games.
Proceedings of the International Conference on Machine Learning, 2023

Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

The Complexity of Markov Equilibrium in Stochastic Games.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
A Note on Model-Free Reinforcement Learning with the Decision-Estimation Coefficient.
CoRR, 2022

Planning in Observable POMDPs in Quasipolynomial Time.
CoRR, 2022

Fast rates for nonparametric online learning: from realizability to learning in games.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Near-optimal no-regret learning for correlated equilibria in multi-player general-sum games.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Learning in Observable POMDPs, without Computationally Intractable Oracles.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Can Q-learning be Improved with Advice?
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Smoothed Online Learning is as Easy as Statistical Learning.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Sample-efficient proper PAC learning with approximate differential privacy.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Littlestone Classes are Privately Online Learnable.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deep Learning with Label Differential Privacy.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Near-Optimal No-Regret Learning in General Games.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy.
Proceedings of the Advances in Cryptology - EUROCRYPT 2021, 2021

Differentially Private Nonparametric Regression Under a Growth Condition.
Proceedings of the Conference on Learning Theory, 2021

Near-tight closure b ounds for the Littlestone and threshold dimensions.
Proceedings of the Algorithmic Learning Theory, 2021

2020
Near-tight closure bounds for Littlestone and threshold dimensions.
CoRR, 2020

Round Complexity of Common Randomness Generation: The Amortized Setting.
Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Tight last-iterate convergence rates for no-regret learning in multi-player games.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Independent Policy Gradient Methods for Competitive Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Pure Differentially Private Summation from Anonymous Messages.
Proceedings of the 1st Conference on Information-Theoretic Cryptography, 2020

Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems.
Proceedings of the Conference on Learning Theory, 2020

2019
On the Power of Multiple Anonymous Messages.
IACR Cryptol. ePrint Arch., 2019

Private Heavy Hitters and Range Queries in the Shuffled Model.
CoRR, 2019

A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation.
Electron. Colloquium Comput. Complex., 2018

Theory of Deep Learning IIb: Optimization Properties of SGD.
CoRR, 2018

Coloring Chains for Compression with Uncertain Priors.
Electron. J. Comb., 2018

Deep Learning for Multi-Facility Location Mechanism Design.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Size-Independent Sample Complexity of Neural Networks.
Proceedings of the Conference On Learning Theory, 2018

2016
The m-degenerate chromatic number of a digraph.
Discret. Math., 2016

2015
Acyclic Subgraphs of Planar Digraphs.
Electron. J. Comb., 2015

2014
Resolving a Conjecture on Degree of Regularity of Linear Homogeneous Equations.
Electron. J. Comb., 2014


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