Scott M. Jordan

According to our database, Scott M. Jordan authored at least 16 papers between 2017 and 2024.

Collaborative distances:

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

2024

A New View on Planning in Online Reinforcement Learning.

Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White

CoRR, 2024

The Cliff of Overcommitment with Policy Gradient Step Sizes.

Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas

RLJ, 2024

Position: Benchmarking is Limited in Reinforcement Learning Research.

Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas

Proceedings of the Forty-first International Conference on Machine Learning, 2024

From Past to Future: Rethinking Eligibility Traces.

Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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

Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model.

Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, Zhihua Zhang

CoRR, 2023

Behavior Alignment via Reward Function Optimization.

Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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

Towards Safe Policy Improvement for Non-Stationary MDPs.

Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Evaluating the Performance of Reinforcement Learning Algorithms.

Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, 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

Learning a Better Negative Sampling Policy with Deep Neural Networks for Search.

Daniel Cohen, Scott M. Jordan, W. Bruce Croft

Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, 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

2018

Distributed Evaluations: Ending Neural Point Metrics.

Daniel Cohen, Scott M. Jordan, W. Bruce Croft

CoRR, 2018

Learning to Use a Ratchet by Modeling Spatial Relations in Demonstrations.

Li Yang Ku, Scott Michael Jordan, Julia Badger, Erik G. Learned-Miller, Rod Grupen

Proceedings of the 2018 International Symposium on Experimental Robotics, 2018

2017

Summary of Experiments in Belief-Space Planning at the Laboratory for Perceptual Robotics.

Scott Michael Jordan, Dirk Ruiken, Tiffany Q. Liu, Takeshi Takahashi, Michael William Lanighan, Roderic A. Grupen

Proceedings of the 2017 AAAI Spring Symposia, 2017