Brahma S. Pavse

Ph.D. Candidate, University of Wisconsin–Madison

pavse@wisc.edu Department of Computer Sciences, University of Wisconsin–Madison

I am a Computer Science Ph.D. candidate at the University of Wisconsin–Madison, where I am advised by Josiah Hanna. Previously, my research was supported by the Cisco Systems Distinguished Graduate Fellowship, and I was a research intern at Netflix Research and Sony AI.

I am broadly interested in building artificial intelligence agents that make decisions and learn from experience, i.e., reinforcement learning. My PhD work has focussed on the critical question of how agents should learn representations of their world to make reliable long-term predictions for optimal decision-making.

Previously, I completed my BS and MS in Computer Science from the University of Texas at Austin, where I was fortunate to be advised by Peter Stone. I also worked as a software engineer at Salesforce and SAS Institute.

Feel free to shoot me an email if you want to chat!

News

[Oct 2025] Named a Top Reviewer at NeurIPS 2025!
[May 2025] I received the Cisco Systems Distinguished Graduate Fellowship!
[May 2025] Our paper “Stable Offline Value Function Learning with Bisimulation-based Representations” was accepted at ICML 2025!
[May 2025] I will be interning at Netflix Research this summer!
[Apr 2025] Passed my prelim exam!

Selected Publications

Stable Offline Value Function Learning with Bisimulation-based Representations
Brahma S. Pavse, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
Proceedings of the 42nd International Conference on Machine Learning (ICML), 2025

PDF Cite

@inproceedings{pavse2025stable,
 archiveprefix = {arXiv},
 author = {Brahma S. Pavse and Yudong Chen and Qiaomin Xie and Josiah P. Hanna},
 booktitle = {Proceedings of the 42nd International Conference on Machine Learning (ICML)},
 eprint = {2410.01643},
 month = {July},
 primaryclass = {cs.LG},
 title = {Stable Offline Value Function Learning with Bisimulation-based Representations},
 url = {https://arxiv.org/abs/2410.01643},
 year = {2025}
}

Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Brahma S. Pavse, Matthew Zurek, Yudong Chen, Qiaomin Xie, Josiah P. Hanna
Proceedings of the 41st International Conference on Machine Learning (ICML), 2024

PDF Code Cite

@inproceedings{pavse2024stabilize,
 archiveprefix = {arXiv},
 author = {Brahma S. Pavse and Matthew Zurek and Yudong Chen and Qiaomin Xie and Josiah P. Hanna},
 booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML)},
 eprint = {2306.01896},
 month = {July},
 primaryclass = {cs.LG},
 title = {Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces},
 url = {https://arxiv.org/abs/2306.01896},
 year = {2024}
}

State-Action Similarity-Based Representations for Off-Policy Evaluation
Brahma S. Pavse, Josiah P. Hanna
Advances in Neural Information Processing Systems (NeurIPS), 2023

PDF Code Cite

@inproceedings{pavse2023rope,
 archiveprefix = {arXiv},
 author = {Brahma S. Pavse and Josiah P. Hanna},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 eprint = {2310.18409},
 month = {December},
 primaryclass = {cs.LG},
 title = {State-Action Similarity-Based Representations for Off-Policy Evaluation},
 url = {https://arxiv.org/abs/2310.18409},
 year = {2023}
}

Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction
Brahma S. Pavse, Josiah P. Hanna
Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023

PDF Cite

@inproceedings{pavse2023absmis,
 address = {Washington, DC, USA},
 author = {Brahma S. Pavse and Josiah P. Hanna},
 booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI)},
 month = {February},
 title = {Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction},
 url = {documents/papers/2023/pavse_2023_absmis.pdf},
 year = {2023}
}