Philip Amortila

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 21

Top Topics

Temporally Extended

Reinforcement Learning

Top Venues

Publications

Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. COLT (2024)
Philip Amortila, Tongyi Cao, Akshay Krishnamurthy
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. CoRR (2024)
Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. CoRR (2024)
Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy
Scalable Online Exploration via Coverability. CoRR (2024)
Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. ICLR (2024)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. CoRR (2023)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. ICML (2023)
Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. NeurIPS (2022)
Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. CoRR (2022)
Gellért Weisz, Philip Amortila, Csaba Szepesvári
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. ALT (2021)
Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. COLT (2021)
Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. CoRR (2021)
Harsh Satija, Philip Amortila, Joelle Pineau
Constrained Markov Decision Processes via Backward Value Functions. CoRR (2020)
Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS (2020)
Harsh Satija, Philip Amortila, Joelle Pineau
Constrained Markov Decision Processes via Backward Value Functions. ICML (2020)
Gellért Weisz, Philip Amortila, Csaba Szepesvári
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. CoRR (2020)
Philip Amortila, Nan Jiang, Tengyang Xie
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. CoRR (2020)
Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR (2020)
Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI (2019)
Philip Amortila, Guillaume Rabusseau
Learning Graph Weighted Models on Pictures. ICGI (2018)
Philip Amortila, Guillaume Rabusseau
Learning Graph Weighted Models on Pictures. CoRR (2018)