Noah Golowich
27 papers · 2018–2026 · 6 venues · across top CS/AI venues
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Research topics
Machine Learning
(24)
Optimization & Theory
(20)
Artificial Intelligence
(14)
Core AI
(14)
Learning Types
(7)
Reinforcement Learning
(6)
Methods
(6)
Application Areas
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Deep Learning
(3)
Mathematics & Optimization
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Core Methods
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Optimization
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Natural Language Processing
(2)
Security & Privacy
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Applications
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Keywords
sample complexity
(6)
regret bound
(5)
reinforcement learning
(4)
no-regret learning
(4)
coarse correlated equilibrium
(3)
differential privacy
(3)
multi-agent reinforcement learning
(3)
interactive decision making
(2)
markov game
(2)
multi-player game
(2)
online learning
(2)
stochastic game
(2)
game theory
(2)
convergence rate
(2)
deep learning
(2)
vc dimension
(2)
gradient descent
(2)
computational complexity
(2)
last-iterate convergence
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decision-estimation coefficient
(2)
Papers
Learning with Simulators: No Regret in a Computationally Bounded World
COLT 2026
The Role of Sparsity for Length Generalization in LLMs
ICML 2025
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
NeurIPS 2024
Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"?
COLT 2024
Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions
COLT 2024
Near-Optimal Learning and Planning in Separated Latent MDPs
COLT 2024
Online Control in Population Dynamics
NeurIPS 2024
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
ICML 2023
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
NeurIPS 2023
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
COLT 2023
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
COLT 2023
The Complexity of Markov Equilibrium in Stochastic Games
COLT 2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
COLT 2023
Can Q-learning be Improved with Advice?
COLT 2022
Learning in Observable POMDPs, without Computationally Intractable Oracles
NeurIPS 2022
Smoothed Online Learning is as Easy as Statistical Learning
COLT 2022
Deep Learning with Label Differential Privacy
NeurIPS 2021
Differentially Private Nonparametric Regression Under a Growth Condition
COLT 2021
Littlestone Classes are Privately Online Learnable
NeurIPS 2021
Near-Optimal No-Regret Learning in General Games
NeurIPS 2021
Near-tight Closure Bounds for the Littlestone and Threshold Dimensions
ALT 2021
Independent Policy Gradient Methods for Competitive Reinforcement Learning
NeurIPS 2020
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
COLT 2020
Tight last-iterate convergence rates for no-regret learning in multi-player games
NeurIPS 2020
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
ICLR 2019
Deep Learning for Multi-Facility Location Mechanism Design
IJCAI 2018
Size-Independent Sample Complexity of Neural Networks
COLT 2018