| 2024 | COLT | Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. | Philip Amortila, Tongyi Cao, Akshay Krishnamurthy |
| 2024 | ICLR | Harnessing Density Ratios for Online Reinforcement Learning. | Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie |
| 2024 | ICML | Scalable Online Exploration via Coverability. | Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy |
| 2023 | ICML | The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. | Philip Amortila, Nan Jiang, Csaba Szepesvri |
| 2021 | ALT | Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. | Gellrt Weisz, Philip Amortila, Csaba Szepesvri |
| 2021 | COLT | On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. | Gellrt Weisz, Philip Amortila, Barnabs Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvri |
| 2020 | AISTATS | A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. | Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare |
| 2020 | ICML | Constrained Markov Decision Processes via Backward Value Functions. | Harsh Satija, Philip Amortila, Joelle Pineau |
| 2019 | AAAI | Temporally Extended Metrics for Markov Decision Processes. | Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup |