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ORCIDJames E. Kostas
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- affiliation: University of Massachusetts, Amherst, MA, USA
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Conference and Workshop Papers
- 2024
[c6]Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas:
The Cliff of Overcommitment with Policy Gradient Step Sizes. RLC 2024: 864-883- 2023
- [c5]Austin Hoag, James E. Kostas, Bruno Castro da Silva, Philip S. Thomas, Yuriy Brun
:
Seldonian Toolkit: Building Software with Safe and Fair Machine Learning. ICSE Companion 2023: 107-111 - 2021
- [c4]James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas:
High Confidence Generalization for Reinforcement Learning. ICML 2021: 5764-5773 - [c3]Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James E. Kostas, Philip S. Thomas, Martha White:
Structural Credit Assignment in Neural Networks using Reinforcement Learning. NeurIPS 2021: 30257-30270 - 2020
- [c2]James E. Kostas, Chris Nota, Philip S. Thomas:
Asynchronous Coagent Networks. ICML 2020: 5426-5435 - 2019
- [c1]Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas:
Learning Action Representations for Reinforcement Learning. ICML 2019: 941-950
Informal and Other Publications
- 2023
- [i5]James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas:
Coagent Networks: Generalized and Scaled. CoRR abs/2305.09838 (2023) - 2021
- [i4]James E. Kostas, Philip S. Thomas, Georgios Theocharous:
Edge-Compatible Reinforcement Learning for Recommendations. CoRR abs/2112.05812 (2021) - 2019
- [i3]Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas:
Learning Action Representations for Reinforcement Learning. CoRR abs/1902.00183 (2019) - [i2]James E. Kostas, Chris Nota, Philip S. Thomas:
Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock. CoRR abs/1902.05650 (2019) - [i1]Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James E. Kostas:
Classical Policy Gradient: Preserving Bellman's Principle of Optimality. CoRR abs/1906.03063 (2019)
Coauthor Index
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