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2020 – today
- 2024
[c11]Philip Amortila, Tongyi Cao, Akshay Krishnamurthy:
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. COLT 2024: 130-160- [c10]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. ICLR 2024 - [c9]Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy:
Scalable Online Exploration via Coverability. ICML 2024 - [i11]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. CoRR abs/2401.09681 (2024) - [i10]Philip Amortila, Tongyi Cao, Akshay Krishnamurthy:
Mitigating Covariate Shift in Misspecified Regression with Applications to Reinforcement Learning. CoRR abs/2401.12216 (2024) - [i9]Philip Amortila, Dylan J. Foster, Akshay Krishnamurthy:
Scalable Online Exploration via Coverability. CoRR abs/2403.06571 (2024) - 2023
- [c8]Philip Amortila, Nan Jiang, Csaba Szepesvári:
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. ICML 2023: 768-790 - [i8]Philip Amortila, Nan Jiang, Csaba Szepesvári:
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. CoRR abs/2307.13332 (2023) - 2022
- [c7]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 - [i7]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 abs/2207.08342 (2022) - 2021
- [c6]Gellért Weisz, Philip Amortila, Csaba Szepesvári:
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. ALT 2021: 1237-1264 - [c5]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: 4355-4385 - [i6]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 abs/2102.02049 (2021) - 2020
- [c4]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. AISTATS 2020: 4357-4366 - [c3]Harsh Satija, Philip Amortila, Joelle Pineau:
Constrained Markov Decision Processes via Backward Value Functions. ICML 2020: 8502-8511 - [i5]Philip Amortila, Doina Precup, Prakash Panangaden, Marc G. Bellemare:
A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms. CoRR abs/2003.12239 (2020) - [i4]Harsh Satija, Philip Amortila, Joelle Pineau:
Constrained Markov Decision Processes via Backward Value Functions. CoRR abs/2008.11811 (2020) - [i3]Gellért Weisz, Philip Amortila, Csaba Szepesvári:
Exponential Lower Bounds for Planning in MDPs With Linearly-Realizable Optimal Action-Value Functions. CoRR abs/2010.01374 (2020) - [i2]Philip Amortila, Nan Jiang, Tengyang Xie:
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. CoRR abs/2011.01075 (2020)
2010 – 2019
- 2019
- [c2]Philip Amortila, Marc G. Bellemare, Prakash Panangaden, Doina Precup:
Temporally Extended Metrics for Markov Decision Processes. SafeAI@AAAI 2019 - 2018
- [c1]Philip Amortila, Guillaume Rabusseau:
Learning Graph Weighted Models on Pictures. ICGI 2018: 104-117 - [i1]Philip Amortila, Guillaume Rabusseau:
Learning Graph Weighted Models on Pictures. CoRR abs/1806.08297 (2018)
Coauthor Index
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