| 2026 | COLT | Learning with Simulators: No Regret in a Computationally Bounded World. | Sasha Voitovych, Abhishek Shetty, Noah Golowich, Alexander Rakhlin |
| 2026 | STOC | Improved Pseudorandom Codes from Permuted Puzzles. | Miranda Christ, Noah Golowich, Sam Gunn, Ankur Moitra, Daniel Wichs |
| 2025 | ICML | The Role of Sparsity for Length Generalization in LLMs. | Noah Golowich, Samy Jelassi, David Brandfonbrener, Sham M. Kakade, Eran Malach |
| 2025 | STOC | Breaking the T^(2/3) Barrier for Sequential Calibration. | Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich, Robert Kleinberg, Princewill Okoroafor |
| 2024 | COLT | Near-Optimal Learning and Planning in Separated Latent MDPs. | Fan Chen, Constantinos Daskalakis, Noah Golowich, Alexander Rakhlin |
| 2024 | COLT | Is Efficient PAC Learning Possible with an Oracle That Responds "Yes" or "No"? | Constantinos Daskalakis, Noah Golowich |
| 2024 | COLT | Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. | Noah Golowich, Ankur Moitra |
| 2024 | FOCS | Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2024 | STOC | From External to Swap Regret 2.0: An Efficient Reduction for Large Action Spaces. | Yuval Dagan, Constantinos Daskalakis, Maxwell Fishelson, Noah Golowich |
| 2024 | STOC | Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2023 | COLT | STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games. | Constantinos Daskalakis, Noah Golowich, Stratis Skoulakis, Emmanouil Zampetakis |
| 2023 | COLT | The Complexity of Markov Equilibrium in Stochastic Games. | Constantinos Daskalakis, Noah Golowich, Kaiqing Zhang |
| 2023 | COLT | On the Complexity of Multi-Agent Decision Making: From Learning in Games to Partial Monitoring. | Dean P. Foster, Dylan J. Foster, Noah Golowich, Alexander Rakhlin |
| 2023 | COLT | Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient. | Dylan J. Foster, Noah Golowich, Yanjun Han |
| 2023 | ICML | Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games. | Dylan J. Foster, Noah Golowich, Sham M. Kakade |
| 2023 | STOC | Planning and Learning in Partially Observable Systems via Filter Stability. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2022 | COLT | Smoothed Online Learning is as Easy as Statistical Learning. | Adam Block, Yuval Dagan, Noah Golowich, Alexander Rakhlin |
| 2022 | COLT | Can Q-learning be Improved with Advice? | Noah Golowich, Ankur Moitra |
| 2022 | STOC | Near-optimal no-regret learning for correlated equilibria in multi-player general-sum games. | Ioannis Anagnostides, Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson, Noah Golowich, Tuomas Sandholm |
| 2022 | STOC | Fast rates for nonparametric online learning: from realizability to learning in games. | Constantinos Daskalakis, Noah Golowich |
| 2021 | ALT | Near-tight closure b ounds for the Littlestone and threshold dimensions. | Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi |
| 2021 | COLT | Differentially Private Nonparametric Regression Under a Growth Condition. | Noah Golowich |
| 2021 | EuroCrypt | On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. | Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker |
| 2021 | STOC | Sample-efficient proper PAC learning with approximate differential privacy. | Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi |
| 2020 | COLT | Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems. | Noah Golowich, Sarath Pattathil, Constantinos Daskalakis, Asuman E. Ozdaglar |
| 2020 | SODA | Round Complexity of Common Randomness Generation: The Amortized Setting. | Noah Golowich, Madhu Sudan |
| 2019 | ICLR | A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks. | Sanjeev Arora, Nadav Cohen, Noah Golowich, Wei Hu |
| 2019 | SODA | Communication-Rounds Tradeoffs for Common Randomness and Secret Key Generation. | Madhu Sudan, Badih Ghazi, Noah Golowich, Mitali Bafna |
| 2018 | COLT | Size-Independent Sample Complexity of Neural Networks. | Noah Golowich, Alexander Rakhlin, Ohad Shamir |
| 2018 | IJCAI | Deep Learning for Multi-Facility Location Mechanism Design. | Noah Golowich, Harikrishna Narasimhan, David C. Parkes |