Random Shuffling Beats SGD after Finite Epochs. - Jeff Z. HaoChen; Suvrit Sra (ICML, 2019)

Meta-learning Transferable Representations with a Single Target Domain. - Hong Liu; Jeff Z. HaoChen; Colin Wei; Tengyu Ma 001 (CoRR, 2020)

Shape Matters: Understanding the Implicit Bias of the Noise Covariance. - Jeff Z. HaoChen; Colin Wei; Jason D. Lee; Tengyu Ma 001 (COLT, 2021)

Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss. - Jeff Z. HaoChen; Colin Wei; Adrien Gaidon; Tengyu Ma 001 (NeurIPS, 2021)

Self-supervised Learning is More Robust to Dataset Imbalance. - Hong Liu; Jeff Z. HaoChen; Adrien Gaidon; Tengyu Ma 001 (ICLR, 2022)

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. - Kendrick Shen; Robbie M. Jones; Ananya Kumar; Sang Michael Xie; Jeff Z. HaoChen; Tengyu Ma 001; Percy Liang (ICML, 2022)

Amortized Proximal Optimization. - Juhan Bae; Paul Vicol; Jeff Z. HaoChen; Roger B. Grosse (NeurIPS, 2022)

Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations. - Jeff Z. HaoChen; Colin Wei; Ananya Kumar; Tengyu Ma 001 (NeurIPS, 2022)

Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models. - Yuhui Zhang; Michihiro Yasunaga; Zhengping Zhou; Jeff Z. HaoChen; James Zou 001; Percy Liang; Serena Yeung (ACL (Findings), 2023)

A theoretical study of inductive biases in contrastive learning. - Jeff Z. HaoChen; Tengyu Ma 001 (ICLR, 2023)

Diagnosing and Rectifying Vision Models using Language. - Yuhui Zhang; Jeff Z. HaoChen; Shih-Cheng Huang; Kuan-Chieh Wang; James Zou 001; Serena Yeung (ICLR, 2023)

Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time. - Arvind V. Mahankali; Jeff Z. HaoChen; Kefan Dong; Margalit Glasgow; Tengyu Ma 001 (CoRR, 2023)