James E. Kostas

According to our database, James E. Kostas authored at least 9 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Coagent Networks: Generalized and Scaled.

James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas

CoRR, 2023

Seldonian Toolkit: Building Software with Safe and Fair Machine Learning.

Austin Hoag, James E. Kostas, Bruno Castro da Silva, Philip S. Thomas, Yuriy Brun

Proceedings of the 45th IEEE/ACM International Conference on Software Engineering: ICSE 2023 Companion Proceedings, 2023

2021

Edge-Compatible Reinforcement Learning for Recommendations.

James E. Kostas, Philip S. Thomas, Georgios Theocharous

CoRR, 2021

Structural Credit Assignment in Neural Networks using Reinforcement Learning.

Dhawal Gupta, Gabor Mihucz, Matthew Schlegel, James E. Kostas, Philip S. Thomas, Martha White

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

High Confidence Generalization for Reinforcement Learning.

James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Asynchronous Coagent Networks.

James E. Kostas, Chris Nota, Philip S. Thomas

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Classical Policy Gradient: Preserving Bellman's Principle of Optimality.

Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James E. Kostas

CoRR, 2019

Asynchronous Coagent Networks: Stochastic Networks for Reinforcement Learning without Backpropagation or a Clock.

James E. Kostas, Chris Nota, Philip S. Thomas

CoRR, 2019

Learning Action Representations for Reinforcement Learning.

Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas

Proceedings of the 36th International Conference on Machine Learning, 2019