Jubayer Ibn Hamid
I am an incoming Ph.D. student, currently doing research at Stanford where I am advised by Chelsea Finn and Dorsa Sadigh. I studied Mathematical Physics (B.S) and Computer Science (M.S), both at Stanford University.
I work in artificial intelligence with a focus on the intersection of reinforcement learning, generative models and representation learning. I am also interested in pure mathematics such as abstract algebra, category theory and algebraic geometry.
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Research
My research interests in building embodied agents are in online reinforcement learning fine-tuning, test-time decoding methods from complex multimodal policies and long-context policies. I am also interested in understanding what kinds of data we should scale for better generalization and why. Recently, I have become interested in sample-efficient methods for preference fine-tuning generative models.
(*) denotes co-first authorship
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Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling. Yuejiang Liu*, Jubayer Ibn Hamid*, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn. International Conference on Learning Representations (ICLR), 2025. (Website)
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Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning. Kyle Hsu*, Jubayer Ibn Hamid*, Kaylee Burns, Chelsea Finn, Jiajun Wu. International Conference on Machine Learning (ICML), 2024.
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What Makes Pre-trained Visual Representations Successful For Robust Manipulation? Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid, Tianhe Yu, Chelsea Finn, Karol Hausman. Conference on Robot Learning (CoRL), 2024.
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Notes
Here are some introductory notes on various topics that have fascinated me. These are not meant to be in-depth. Rather, they are meant to cover some of the basic constructions that show up periodically and are also interesting in and of themselves.
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Talks
Bidirectional Decoding. OpenAI. 25th February, 2025.
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