Label Noise SGD Provably Prefers Flat Global Minimizers. - Alex Damian; Tengyu Ma 001; Jason D. Lee (NeurIPS, 2021)

Neural Networks can Learn Representations with Gradient Descent. - Alex Damian; Jason D. Lee; Mahdi Soltanolkotabi (CoRR, 2022)

Self-Stabilization: The Implicit Bias of Gradient Descent at the Edge of Stability. - Alex Damian; Eshaan Nichani; Jason D. Lee (ICLR, 2023)

Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models. - Alex Damian; Eshaan Nichani; Rong Ge 001; Jason D. Lee (NeurIPS, 2023)

Fine-Tuning Language Models with Just Forward Passes. - Sadhika Malladi; Tianyu Gao 001; Eshaan Nichani; Alex Damian; Jason D. Lee; Danqi Chen 001; Sanjeev Arora (NeurIPS, 2023)

Provable Guarantees for Nonlinear Feature Learning in Three-Layer Neural Networks. - Eshaan Nichani; Alex Damian; Jason D. Lee (NeurIPS, 2023)

Computational-Statistical Gaps in Gaussian Single-Index Models (Extended Abstract). - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (COLT, 2024)

How Transformers Learn Causal Structure with Gradient Descent. - Eshaan Nichani; Alex Damian; Jason D. Lee (ICML, 2024)

Computational-Statistical Gaps in Gaussian Single-Index Models. - Alex Damian; Loucas Pillaud-Vivien; Jason D. Lee; Joan Bruna (CoRR, 2024)

Learning Compositional Functions with Transformers from Easy-to-Hard Data. - Zixuan Wang; Eshaan Nichani; Alberto Bietti; Alex Damian; Daniel Hsu 001; Jason D. Lee; Denny Wu (COLT, 2025)

Understanding Optimization in Deep Learning with Central Flows. - Jeremy Cohen 001; Alex Damian; Ameet Talwalkar; J. Zico Kolter; Jason D. Lee (ICLR, 2025)

The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models. - Alex Damian; Jason D. Lee; Joan Bruna (CoRR, 2025)