Yiding Jiang

yd <last name> at cmu dot edu

I am a PhD student at 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 bachelor of science in Electrical Engineering and Computer Science at UC Berkeley, where I worked on robotics and generative models advised by Professor Ken Goldberg. I have also spent time as a research intern at Meta AI Research and Cerebras Systems.

Google Scholar / GitHub / Twitter / LinkedIn

Research

I am interested in understanding the science of deep learning, and using the insights to improve the models further. My research spans representation learning, reinforcement learning, and generalization -- both concrete generalization bounds and also less well-understood empirical phenomena like out-of-distribution and zero-shot generalization. One of my current focus is to identify structural assumptions of real-world data, and how they enable efficient 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.

Preprint

* indicates equal contribution

	Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws Yiding Jiang, Allan Zhou, Zhili Feng, Sadhika Malladi, J. Zico Kolter Arxiv, 2024 [code]
	Improving Generalization on the ProcGen Benchmark with Simple Architectural Changes and Scale Andrew Jesson, Yiding Jiang Arxiv, 2024 [code]
	Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation Yutong He, Alexander Robey, Naoki Murata, Yiding Jiang, Joshua Williams, George J. Pappas, Hamed Hassani, Yuki Mitsufuji, Ruslan Salakhutdinov, J. Zico Kolter Arxiv, 2024

Publication

	On the Joint Interaction of Models, Data, and Features Yiding Jiang, Christina Baek, J. Zico Kolter ICLR, 2024 (oral)
	Understanding prompt engineering may not require rethinking generalization Victor Akinwande, Yiding Jiang, Dylan Sam, J. Zico Kolter ICLR, 2024 Sampling and Optimization in Discrete Space workshop, ICML 2023 (Outstanding Paper)
	Language models are weak learners Hariharan Manikandan, Yiding Jiang, J. Zico Kolter NeurIPS, 2023
	Neural Functional Transformers Allan Zhou, Kaien Yang, Yiding Jiang, Kaylee Burns, Winnie Xu, Samuel Sokota, J. Zico Kolter, Chelsea Finn NeurIPS, 2023
	Permutation equivariant neural functionals Allan Zhou, Kaien Yang, Kaylee Burns, Adriano Cardace, Yiding Jiang, Samuel Sokota, J. Zico Kolter, Chelsea Finn NeurIPS, 2023
	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]
	Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift Christina Baek, Yiding Jiang, Aditi Raghunathan, J. Zico Kolter NeurIPS, 2022 (oral)
	Assessing Generalization of SGD via Disagreement Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J. Zico Kolter ICLR, 2022 (spotlight) [blog post]
	Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning Yiding Jiang, Parth Natekar, Manik Sharma, Sumukh K Aithal, Dhruva Kashyap, Natarajan Subramanyam,Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, Pierre Foret, Scott Yak, Hossein Mobahi, Behnam Neyshabur, Samy Bengio PMLR: NeurIPS 2020 Competition and Demonstration Track*, 2020 [competiton page] [Codalab] [competition dataset] [competition code]
	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)
	Observational Overfitting in Reinforcement Learning Xingyou Song, Yiding Jiang, Stephen Tu, Yilun Du, Behnam Neyshabur ICLR, 2020
	Language as an Abstraction for Hierarchical Deep Reinforcement Learning Yiding Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn NeurIPS, 2019 [project page] [environment]
	Adversarial Grasp Objects David Wang, David Tseng, Pusong Li, Yiding Jiang, Menglong Guo, Michael Danielczuk, Jeffrey Mahler, Jeffrey Ichnowski, Ken Goldberg CASE, 2019 IEEE Spectrum article
	Predicting the Generalization Gap in Deep Networks with Margin Distributions Yiding Jiang, Dilip Krishnan, Hossein Mobahi, Samy Bengio ICLR, 2019 blog post

Technical Report

Ask & Explore: Grounded Question Answering for Curiosity-Driven Exploration
Jivat Neet Kaur, Yiding Jiang, Paul Pu Liang
Arxiv, 2021
Workshop on Embodied Multimodal Learning, ICLR 2021

Teaching

Teaching Assistant, 10-708 Probablistic 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. University of California, Berkeley. Fall 2017.

Project

CLEVR-Robot Environment
GitHub repository

The CLEVR-Robot environment is a reinforcement learning environment that aims to provide a research platform for developing RL agents at the intersection of vision, language, and continuous/discrete control.

Deep Model Generalization Dataset (DEMOGEN)
GitHub repository

The DEMOGEN dataset is a the collection of 756 trained neural network models and the code to use them. This is the same dataset used by our work "Predicting the Generalization Gap in Deep Networks with Margin Distributions", and is to our knoweledge the first dataset of models for studying generalization.

City2City
Project Page

City2City is a project that restyles Google streetview images of one city with another city.

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