Noah Golowich
26 papers · 2018–2025 · 6 conferences · across top CS/AI conferences
Achievements
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🐝 Cross-Pollinator (13) 🧭 Keyword Pioneer 🏃 Academic Marathon (7) 🌍 Conference Polyglot (6) 🌈 Renaissance Researcher (7)
🏃
Academic Marathon
(7)
🧭
Keyword Pioneer
🐝
Cross-Pollinator
(13)
🔥
Unstoppable
(8)
⚡
Prolific Year
(5)
💎
Century Club
(26)
❓
The Questioner
(2)
🗃️
Keyword Collector
(103)
Conferences
COLT (12)
NIPS (9)
ICML (2)
ALT (1)
ICLR (1)
IJCAI (1)
Top co-authors
Research topics
Keywords
regret bound
(5)
reinforcement learning
(3)
differential privacy
(3)
multi-agent reinforcement learning
(3)
no-regret learning
(3)
coarse correlated equilibrium
(3)
game theory
(2)
sample complexity
(2)
interactive decision making
(2)
markov game
(2)
convergence rate
(2)
computational complexity
(2)
stochastic game
(2)
deep learning
(2)
multi-player game
(2)
policy learning
(1)
binary classification
(1)
pac learning
(1)
online learning
(1)
function approximation
(1)
Papers
The Role of Sparsity for Length Generalization in LLMs
ICML 2025
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
Edit Distance Robust Watermarks via Indexing Pseudorandom Codes
NIPS 2024
Online Control in Population Dynamics
NIPS 2024
Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"?
COLT 2024
On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring
COLT 2023
The Complexity of Markov Equilibrium in Stochastic Games
COLT 2023
STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games
COLT 2023
Model-Free Reinforcement Learning with the Decision-Estimation Coefficient
NIPS 2023
Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
ICML 2023
Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient
COLT 2023
Learning in Observable POMDPs, without Computationally Intractable Oracles
NIPS 2022
Smoothed Online Learning is as Easy as Statistical Learning
COLT 2022
Can Q-learning be Improved with Advice?
COLT 2022
Differentially Private Nonparametric Regression Under a Growth Condition
COLT 2021
Littlestone Classes are Privately Online Learnable
NIPS 2021
Deep Learning with Label Differential Privacy
NIPS 2021
Near-Optimal No-Regret Learning in General Games
NIPS 2021
Near-tight Closure Bounds for the Littlestone and Threshold Dimensions
ALT 2021
Independent Policy Gradient Methods for Competitive Reinforcement Learning
NIPS 2020
Tight last-iterate convergence rates for no-regret learning in multi-player games
NIPS 2020
Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems
COLT 2020
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks
ICLR 2019
Size-Independent Sample Complexity of Neural Networks
COLT 2018
Deep Learning for Multi-Facility Location Mechanism Design
IJCAI 2018