SGD on Neural Networks Learns Functions of Increasing Complexity. - Preetum Nakkiran; Gal Kaplun; Dimitris Kalimeris; Tristan Yang; Benjamin L. Edelman; Fred Zhang; Boaz Barak (NeurIPS, 2019)

Minimum Cut and Minimum k-Cut in Hypergraphs via Branching Contractions. - Kyle Fox; Debmalya Panigrahi; Fred Zhang (SODA, 2019)

A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates. - Zhixian Lei; Kyle Luh; Prayaag Venkat; Fred Zhang (COLT, 2020)

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. - Samuel B. Hopkins 001; Jerry Li 001; Fred Zhang (NeurIPS, 2020)

Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms. - Alexander Wei 001; Fred Zhang (NeurIPS, 2020)

Faster Fundamental Graph Algorithms via Learned Predictions. - Justin Y. Chen; Sandeep Silwal; Ali Vakilian; Fred Zhang (ICML, 2022)

Optimal Query Complexities for Dynamic Trace Estimation. - David P. Woodruff; Fred Zhang; Richard Zhang 003 (NeurIPS, 2022)

Bayesian Strategy-Proof Facility Location via Robust Estimation. - Emmanouil Zampetakis; Fred Zhang (AISTATS, 2023)

Robust Algorithms on Adaptive Inputs from Bounded Adversaries. - Yeshwanth Cherapanamjeri; Sandeep Silwal; David P. Woodruff; Fred Zhang; Qiuyi Zhang 001; Samson Zhou (ICLR, 2023)

Constant Approximation for Individual Preference Stable Clustering. - Anders Aamand; Justin Y. Chen; Allen Liu; Sandeep Silwal; Pattara Sukprasert; Ali Vakilian; Fred Zhang (NeurIPS, 2023)

On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds. - David P. Woodruff; Fred Zhang; Samson Zhou (NeurIPS, 2023)

Online Prediction in Sub-linear Space. - Binghui Peng; Fred Zhang (SODA, 2023)

Privately Estimating a Gaussian: Efficient, Robust, and Optimal. - Daniel Alabi; Pravesh K. Kothari; Pranay Tankala; Prayaag Venkat; Fred Zhang (STOC, 2023)

Streaming Algorithms for Learning with Experts: Deterministic Versus Robust. - David P. Woodruff; Fred Zhang; Samson Zhou (CoRR, 2023)

Minimum Cut and Minimum k-Cut in Hypergraphs via Branching Contractions. - Kyle Fox; Debmalya Panigrahi; Fred Zhang (ACM Trans. Algorithms, 2023)

Adaptive Regret for Bandits Made Possible: Two Queries Suffice. - Zhou Lu; Qiuyi Zhang 001; Xinyi Chen 001; Fred Zhang; David P. Woodruff; Elad Hazan (ICLR, 2024)

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods. - Fred Zhang; Neel Nanda (ICLR, 2024)

Approaching Human-Level Forecasting with Language Models. - Danny Halawi; Fred Zhang; Chen Yueh-Han; Jacob Steinhardt (NeurIPS, 2024)

ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities. - Ezra Karger; Houtan Bastani; Chen Yueh-Han; Zachary Jacobs; Danny Halawi; Fred Zhang; Philip Tetlock (ICLR, 2025)

Beyond the Rosetta Stone: Unification Forces in Generalization Dynamics. - Carter Blum; Katja Filippova; Ann Yuan; Asma Ghandeharioun; Julian Zimmert; Fred Zhang; Jessica Hoffmann; Tal Linzen; Martin Wattenberg; Lucas Dixon; Mor Geva (CoRR, 2025)