Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2019)

Agnostic Learning of a Single Neuron with Gradient Descent. - Spencer Frei; Yuan Cao 006; Quanquan Gu (NeurIPS, 2020)

Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise. - Spencer Frei; Yuan Cao 006; Quanquan Gu (ICML, 2021)

Provable Robustness of Adversarial Training for Learning Halfspaces with Noise. - Difan Zou; Spencer Frei; Quanquan Gu (ICML, 2021)

Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent. - Spencer Frei; Quanquan Gu (NeurIPS, 2021)

Statistical Learning with Neural Networks Trained by Gradient Descent. - Spencer Frei (2021)

Self-training Converts Weak Learners to Strong Learners in Mixture Models. - Spencer Frei; Difan Zou; Zixiang Chen; Quanquan Gu (AISTATS, 2022)

Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data. - Spencer Frei; Niladri S. Chatterji; Peter L. Bartlett (COLT, 2022)

Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nathan Srebro (COLT, 2023)

Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nathan Srebro; Wei Hu (ICLR, 2023)

The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks. - Spencer Frei; Gal Vardi; Peter L. Bartlett; Nati Srebro (NeurIPS, 2023)

The Effect of SGD Batch Size on Autoencoder Learning: Sparsity, Sharpness, and Feature Learning. - Nikhil Ghosh; Spencer Frei; Wooseok Ha; Bin Yu 001 (CoRR, 2023)

Random Feature Amplification: Feature Learning and Generalization in Neural Networks. - Spencer Frei; Niladri S. Chatterji; Peter L. Bartlett (J. Mach. Learn. Res., 2023)

Benign Overfitting and Grokking in ReLU Networks for XOR Cluster Data. - Zhiwei Xu; Yutong Wang 002; Spencer Frei; Gal Vardi; Wei Hu (ICLR, 2024)

Minimum-Norm Interpolation Under Covariate Shift. - Neil Mallinar; Austin Zane; Spencer Frei; Bin Yu 001 (ICML, 2024)

Benign Overfitting in Single-Head Attention. - Roey Magen; Shuning Shang; Zhiwei Xu; Spencer Frei; Wei Hu; Gal Vardi (CoRR, 2024)

Adversarial Robustness of In-Context Learning in Transformers for Linear Regression. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (CoRR, 2024)

Trained Transformers Learn Linear Models In-Context. - Ruiqi Zhang; Spencer Frei; Peter L. Bartlett (J. Mach. Learn. Res., 2024)

Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context. - Spencer Frei; Gal Vardi (ICLR, 2025)

Understanding In-Context Learning of Linear Models in Transformers Through an Adversarial Lens. - Usman Anwar; Johannes von Oswald; Louis Kirsch; David Krueger 001; Spencer Frei (Trans. Mach. Learn. Res., 2025)

Benign Overfitting and the Geometry of the Ridge Regression Solution in Binary Classification. - Alexander Tsigler; Luiz F. O. Chamon; Spencer Frei; Peter L. Bartlett (CoRR, 2025)