Preprints

1. Model Selection for Off-Policy Evaluation: New Algorithms and Experimental Protocol

   Pai Liu, Lingfeng Zhao, Shivangi Agarwal, Jinghan Liu, Audrey Huang, Philip Amortila, Nan Jiang [arXiv]

Conference Papers

1. Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity

   Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi

   NeurIPS 2024 Oral Presentation [arXiv, talk]

2. Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning

   Philip Amortila, Tongyi Cao, Akshay Krishnamurthy

   COLT 2024 [arXiv]

3. Scalable Online Exploration via Coverability

   Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy

   ICML 2024 [arXiv, talk]

4. Harnessing Density Ratios for Online Reinforcement Learning

   Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie

   ICLR 2024 Spotlight Presentation [arXiv]

5. The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation

   Philip Amortila, Nan Jiang, Csaba Szepesvari

   ICML 2023 [arXiv]

6. A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation 

   Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster

   NeurIPS 2022 [arXiv, talk]

7. On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function

   Gellert Weisz, Philip Amortila, Barnabàs Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári 

   COLT 2021 [arXiv, talk]

8. Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions

   Gellert Weisz, Philip Amortila, Csaba Szepesvári

   ALT 2021 Best Student Paper Award [arXiv, talk]

9. Solving Constrained Markov Decision Processes via Backward Value Functions

   Harsh Satija, Philip Amortila, Joelle Pineau

   ICML 2020 [arXiv, talk]

10. A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

    Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare

    AISTATS 2020 [arXiv, talk] & NeurIPS 2019 Optimization in RL Workshop Spotlight [talk]

11. Learning Graph Weighted Models on Pictures

    Philip Amortila, Guillaume Rabusseau

    ICGI 2018 [arXiv]

Workshop Papers

1.  Temporally Extended Metrics for Markov Decision Processes

   Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup

   AAAI 2019 Safety in AI Workshop Spotlight [pdf]

Technical Notes

1. A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting

   Philip Amortila, Nan Jiang, Tengyang Xie [arXiv]