Yiding Jiang

yd <last name> at cmu dot edu

I am a PhD student at the Machine Learning Department of Carnegie Mellon University, where I work with Professor Zico Kolter. My research is supported by the Google PhD Fellowship.

Previously, I was an AI Resident at Google Research. I obtained my B.S. in Electrical Engineering and Computer Science at UC Berkeley, where I worked on robotics and generative models with Professor Ken Goldberg . I have also spent time as a research intern at Meta AI Research and Cerebras Systems.

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Research interests

I am interested in understanding the science of deep learning, and using the insights to improve the models further. My research spans a wide range of topics including representation learning, reinforcement learning, and generalization — both concrete generalization bounds and less well-understood empirical phenomena like out-of-distribution and zero-shot generalization.

One of my current interests is to identify unique structural properties of real-world data that facilitate deep learning. I am also interested in studying exploration as a mechanism to improve generalization by driving models to acquire diverse, informative data and adapt to dynamic environments.

* indicates equal contribution

Training a Generally Curious Agent
Fahim Tajwar*, Yiding Jiang*, Abitha Thankaraj, Sumaita Sadia Rahman, J. Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov
ICML, 2025 (oral) |  [Website] [code] [SFT dataset] [DPO dataset]

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
Yiding Jiang*, Allan Zhou*, Zhili Feng, Sadhika Malladi, J. Zico Kolter
ICLR, 2025  |  [code] [blog post]

On the Joint Interaction of Models, Data, and Features
Yiding Jiang, Christina Baek, J. Zico Kolter
ICLR, 2024 (oral)

On the Importance of Exploration for Generalization in Reinforcement Learning
Yiding Jiang, J. Zico Kolter, Roberta Raileanu
NeurIPS, 2023  |  [code]

Learning Options via Compression
Yiding Jiang*, Evan Z. Liu*, Benjamin Eysenbach, J. Zico Kolter, Chelsea Finn
NeurIPS, 2022  |  [code]

Assessing Generalization of SGD via Disagreement
Yiding Jiang*, Vaishnavh Nagarajan*, Christina Baek, J. Zico Kolter
ICLR, 2022 (spotlight)  |  [blog post]

Fantastic Generalization Measures and Where to Find Them
Yiding Jiang*, Behnam Neyshabur*, Hossein Mobahi, Dilip Krishnan, Samy Bengio
ICLR, 2020
"Science meets the Engineering of Deep Learning" workshop, NeurIPS 2019 (oral)

Language as an Abstraction for Hierarchical Deep Reinforcement Learning
Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
NeurIPS, 2019  |  [project page] [environment]

Teaching

• Teaching Assistant, 10-708 Probabilistic Graphical Models. Carnegie Mellon University. Fall 2022.
• Teaching Assistant, 10-725 Convex Optimization. Carnegie Mellon University. Fall 2021.
• Reader, CS170 Efficient Algorithms and Intractable Problems. UC Berkeley. Fall 2017.