Scott M. Jordan

22 papers A* 7Misc 1Journal 14

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Resolving UnderEdit & OverEdit with Iterative & Neighbor-Assisted Model Editing. CoRR Bhiman Kumar Baghel, Scott M. Jordan, Zheyuan Ryan Shi, Xiang Lorraine Li
2024	J	`jnl`	A New View on Planning in Online Reinforcement Learning. CoRR Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White
2024	A*	`conf`	From Past to Future: Rethinking Eligibility Traces. AAAI Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
2024	J	`jnl`	Goal-Space Planning with Subgoal Models. J. Mach. Learn. Res. Chunlok Lo, Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Gabor Mihucz, Farzane Aminmansour, Martha White
2024	A*	`conf`	Position: Benchmarking is Limited in Reinforcement Learning Research. ICML Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
2024	J	`jnl`	Position: Benchmarking is Limited in Reinforcement Learning Research. CoRR Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
2024	J	`jnl`	The Cliff of Overcommitment with Policy Gradient Step Sizes. RLJ Scott M. Jordan, Samuel Neumann, James E. Kostas, Adam White, Philip S. Thomas
2023	J	`jnl`	Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model. CoRR Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, Zhihua Zhang
2023	A*	`conf`	Behavior Alignment via Reward Function Optimization. NeurIPS Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva
2023	J	`jnl`	Behavior Alignment via Reward Function Optimization. CoRR Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno Castro da Silva
2023	J	`jnl`	Coagent Networks: Generalized and Scaled. CoRR James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas
2023	J	`jnl`	From Past to Future: Rethinking Eligibility Traces. CoRR Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
2021	A*	`conf`	High Confidence Generalization for Reinforcement Learning. ICML James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas
2020	A*	`conf`	Evaluating the Performance of Reinforcement Learning Algorithms. ICML Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
2020	J	`jnl`	Evaluating the Performance of Reinforcement Learning Algorithms. CoRR Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
2020	A*	`conf`	Towards Safe Policy Improvement for Non-Stationary MDPs. NeurIPS Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
2020	J	`jnl`	Towards Safe Policy Improvement for Non-Stationary MDPs. CoRR Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
2019	J	`jnl`	Classical Policy Gradient: Preserving Bellman's Principle of Optimality. CoRR Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James E. Kostas
2019	A*	`conf`	Learning Action Representations for Reinforcement Learning. ICML Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas
2019	J	`jnl`	Learning Action Representations for Reinforcement Learning. CoRR Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas
2019	Misc	`conf`	Learning a Better Negative Sampling Policy with Deep Neural Networks for Search. ICTIR Daniel Cohen, Scott M. Jordan, W. Bruce Croft
2018	J	`jnl`	Distributed Evaluations: Ending Neural Point Metrics. CoRR Daniel Cohen, Scott M. Jordan, W. Bruce Croft