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ORCIDScott M. Jordan
Person information
- affiliation: University of Alberta, Canada
- affiliation (former): University of Massachusetts, MA, USA
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Conference and Workshop Papers
- 2024
[c9]Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva:
From Past to Future: Rethinking Eligibility Traces. AAAI 2024: 12253-12260- 2023
- [c8]Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva:
Behavior Alignment via Reward Function Optimization. NeurIPS 2023 - 2021
- [c7]James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas:
High Confidence Generalization for Reinforcement Learning. ICML 2021: 5764-5773 - 2020
- [c6]Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas:
Evaluating the Performance of Reinforcement Learning Algorithms. ICML 2020: 4962-4973 - [c5]Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas:
Towards Safe Policy Improvement for Non-Stationary MDPs. NeurIPS 2020 - 2019
- [c4]Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas:
Learning Action Representations for Reinforcement Learning. ICML 2019: 941-950 - [c3]Daniel Cohen, Scott M. Jordan, W. Bruce Croft:
Learning a Better Negative Sampling Policy with Deep Neural Networks for Search. ICTIR 2019: 19-26 - 2018
- [c2]Li Yang Ku, Scott Michael Jordan, Julia Badger, Erik G. Learned-Miller, Rod Grupen:
Learning to Use a Ratchet by Modeling Spatial Relations in Demonstrations. ISER 2018: 398-410 - 2017
- [c1]Scott Michael Jordan, Dirk Ruiken, Tiffany Q. Liu, Takeshi Takahashi, Michael William Lanighan, Roderic A. Grupen:
Summary of Experiments in Belief-Space Planning at the Laboratory for Perceptual Robotics. AAAI Spring Symposia 2017
Informal and Other Publications
- 2024
- [i11]Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White:
A New View on Planning in Online Reinforcement Learning. CoRR abs/2406.01562 (2024) - [i10]Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas:
Position: Benchmarking is Limited in Reinforcement Learning Research. CoRR abs/2406.16241 (2024) - 2023
- [i9]Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, Zhihua Zhang:
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model. CoRR abs/2302.01248 (2023) - [i8]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) - [i7]Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno Castro da Silva:
Behavior Alignment via Reward Function Optimization. CoRR abs/2310.19007 (2023) - [i6]Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva:
From Past to Future: Rethinking Eligibility Traces. CoRR abs/2312.12972 (2023) - 2020
- [i5]Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas:
Evaluating the Performance of Reinforcement Learning Algorithms. CoRR abs/2006.16958 (2020) - [i4]Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas:
Towards Safe Policy Improvement for Non-Stationary MDPs. CoRR abs/2010.12645 (2020) - 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]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) - 2018
- [i1]Daniel Cohen, Scott M. Jordan, W. Bruce Croft:
Distributed Evaluations: Ending Neural Point Metrics. CoRR abs/1806.03790 (2018)
Coauthor Index
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