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Publications
- 2023
[i40]Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip S. Thomas:
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. CoRR abs/2301.10330 (2023)- 2022
- [c48]Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas:
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. NeurIPS 2022 - 2021
- [c41]Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. NeurIPS 2021: 27475-27490 - [i30]Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas:
Universal Off-Policy Evaluation. CoRR abs/2104.12820 (2021) - 2019
- [c24]Blossom Metevier, Stephen Giguere, Sarah Brockman, Ari Kobren, Yuriy Brun, Emma Brunskill, Philip S. Thomas:
Offline Contextual Bandits with High Probability Fairness Guarantees. NeurIPS 2019: 14893-14904 - 2018
- [c23]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Gradient-Like Learning Rules from Representations. ICML 2018: 4924-4932 - [c22]Shayan Doroudi
, Philip S. Thomas, Emma Brunskill:
Importance Sampling for Fair Policy Selection. IJCAI 2018: 5239-5243 - 2017
- [c21]Philip S. Thomas, Emma Brunskill:
Importance Sampling with Unequal Support. AAAI 2017: 2646-2652 - [c20]Philip S. Thomas, Georgios Theocharous, Mohammad Ghavamzadeh, Ishan Durugkar, Emma Brunskill:
Predictive Off-Policy Policy Evaluation for Nonstationary Decision Problems, with Applications to Digital Marketing. AAAI 2017: 4740-4745 - [c18]Zhaohan Guo, Philip S. Thomas, Emma Brunskill:
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation. NIPS 2017: 2492-2501 - [c17]Shayan Doroudi, Philip S. Thomas, Emma Brunskill:
Importance Sampling for Fair Policy Selection. UAI 2017 - [i10]Zhaohan Daniel Guo, Philip S. Thomas, Emma Brunskill:
Using Options for Long-Horizon Off-Policy Evaluation. CoRR abs/1703.03453 (2017) - [i9]Philip S. Thomas, Christoph Dann, Emma Brunskill:
Decoupling Learning Rules from Representations. CoRR abs/1706.03100 (2017) - [i7]Philip S. Thomas, Emma Brunskill:
Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines. CoRR abs/1706.06643 (2017) - [i6]Philip S. Thomas, Bruno Castro da Silva, Andrew G. Barto, Emma Brunskill:
On Ensuring that Intelligent Machines Are Well-Behaved. CoRR abs/1708.05448 (2017) - 2016
- [c15]Philip S. Thomas, Emma Brunskill:
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. ICML 2016: 2139-2148 - [c14]Philip S. Thomas, Bruno Castro da Silva, Christoph Dann, Emma Brunskill:
Energetic Natural Gradient Descent. ICML 2016: 2887-2895 - [i5]Philip S. Thomas, Emma Brunskill:
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning. CoRR abs/1604.00923 (2016) - [i4]Philip S. Thomas, Emma Brunskill:
Importance Sampling with Unequal Support. CoRR abs/1611.03451 (2016)
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