Jeff Z. HaoChen

Email: jhaochen [at]

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I'm a third-year PhD student in Computer Science at Stanford University, affiliated with Stanford AI Lab. I am fortunate to be advised by Tengyu Ma. My current research interests broadly lie in machine learning, particularly deep learning theory, representation learning, and optimization.

In the past, I have had the opportunity to work with Suvrit Sra on convex optimization, and with Roger Grosse on optimization for neural networks.

Before Stanford, I was an undergraduate at Yao Class led by Professor Andrew Chi-Chih Yao at Tsinghua University.

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations
Jeff Z. HaoChen, Colin Wei, Ananya Kumar, Tengyu Ma
NeurIPS, 2022 [PDF]
Amortized Proximal Optimization
Juhan Bae, Paul Vicol, Jeff Z. HaoChen, Roger Grosse
NeurIPS, 2022 [PDF]
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang
ICML, 2022 [PDF]
Self-supervised Learning is More Robust to Dataset Imbalance
Hong Liu, Jeff Z. HaoChen, Adrien Gaidon, Tengyu Ma
ICLR, 2022 (spotlight) [PDF]
Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss
Jeff Z. HaoChen, Colin Wei, Adrien Gaidon, Tengyu Ma
NeurIPS, 2021 (oral) [PDF]
Meta-learning Transferable Representations with a Single Target Domain
Hong Liu, Jeff Z. HaoChen, Colin Wei, Tengyu Ma
Preprint, 2020 [PDF]
Shape Matters: Understanding the Implicit Bias of the Noise Covariance
Jeff Z. HaoChen, Colin Wei, Jason D. Lee, Tengyu Ma
COLT, 2021 [PDF]
Random Shuffling Beats SGD after Finite Epochs
Jeff Z. HaoChen, Suvrit Sra
ICML, 2019 [PDF]