Sikata Sengupta

Replicable Reinforcement Learning with Linear Function Approximation Replication of experimental results has been a challenge faced by many scientific disciplines, including the field of machine learning. Recent work on the theory of machine learning has formalized rep...

I think I posted about it before but never with a thread. We recently put a new preprint on arxiv.

📖 Replicable Reinforcement Learning with Linear Function Approximation

🔗 arxiv.org/abs/2509.08660

In this paper, we study formal replicability in RL with linear function approximation. The... (1/6)

Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces In traditional reinforcement learning (RL), the learner aims to solve a single objective optimization problem: find the policy that maximizes expected reward. However, in many real-world settings, it ...

Come say hi at our #ICML poster today during Poster Session 1 (W-600)! Joint work with @ericeaton.bsky.social @marcelhussing.bsky.social @optimistsinc.bsky.social @aaroth.bsky.social @mkearnsphilly.bsky.social!

arxiv.org/abs/2502.11828

Academic Productivity with GenAI: A Researcher’s Guide My Everyday Use of GenAI as a Researcher

hi bluesky 👋 I’m starting a blog! First post on how I use GenAI in my workflow as an academic. give it a read + tell me what you think:
yeganeha.substack.com/p/academic-p... #GenAI #Academia

Dhruv Rohatgi will be giving a lecture on our recent work on comp-stat tradeoffs in next-token prediction at the RL Theory virtual seminar series (rl-theory.bsky.social) tomorrow at 2pm EST! Should be a fun talk---come check it out!!

Later today, Sikata and Marcel will talk about their recent work on oracle-efficient RL with ensembles. Join us!

Last seminars before the summer break:

04/29: Max Simchowitz (CMU)
05/06: Jeongyeol Kwon (Univ. of Widsconsin-Madison)
05/20: Sikata Sengupta & Marcel Hussing (Univ. of Pennsylvania)
05/27: Dhruv Rohatgi (MIT)
06/03: David Janz (Univ. of Oxford)
06/10: Nneka Okolo (MIT)

@mkearnsphilly.bsky.social is now on bsky as well!

@mkearnsphilly.bsky.social

If you are at #NeurIPS, we will be presenting this work (#6610) from 4:30-7:30PM today and would love to chat! @marcelhussing.bsky.social @optimistsinc.bsky.social @aaroth.bsky.social

Thank you so much!

Thank you so much for making this list! Could I also request @marcelhussing.bsky.social to be added by any chance?

Thanks so much @antoine-mln.bsky.social!

I made a starter pack for learning theory people to gather some people around the topic. There are too many names on here that I don't know so I only added a few I do. If you believe you should be on this list, let me know. I will add people with accurate profile descriptions.

go.bsky.app/21nFz12

Oracle-Efficient Reinforcement Learning for Max Value Ensembles Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardina...

Actual content post: Have not talked much about this work yet but we have a paper on Oracle-Efficient Reinforcement Learning for Max Value Ensembles at this year's #NeurIPS. We provide an efficient algorithm to ensemble policies given a value function oracle. arxiv.org/abs/2405.16739