I am a researcher in the machine learning department at NEC Labs North America. I am broadly interested in algorithm development for core ML with theoretical guarantees.

I completed my PhD in mathematics at Stanford University in 2023, co-advised by James Zou and Lexing Ying.

I can be reached at zach at nec-labs dot com.


Publications

Monitoring AI-Modified Content at Scale [Article 1] [2] [3] [4]
Weixin Liang, Zachary Izzo, Yaohui Zhang, et al.
ICML 2024 (Oral)

Provable Membership Inference Privacy
Zachary Izzo, Jinsung Yoon, Sercan Arik, James Zou
TMLR 2024

Continuous-in-Time Limit for Bayesian Bandits
Yuhua Zhu, Zachary Izzo, Lexing Ying
JMLR 2023

Data-Driven Subgroup Identification for Linear Regression
Zachary Izzo, Ruishan Liu, James Zou
ICML 2023

How to Learn when Data Gradually Reacts to Your Model
Zachary Izzo, James Zou, Lexing Ying
AISTATS 2022

Dimensionality Reduction for Wasserstein Barycenter
Zachary Izzo, Sandeep Silwal, Samson Zhou
NeurIPS 2021

How to Learn when Data Reacts to Your Model: Performative Gradient Descent
Zachary Izzo, Lexing Ying, James Zou
ICML 2021

Borrowing From the Future: Addressing Double Sampling in Model-free Control
Yuhua Zhu, Zachary Izzo, Lexing Ying
MSML 2021

Approximate Data Deletion from Machine Learning Models [Article]
Zachary Izzo, Mary Anne Smart, Kamalika Chaudhuri, James Zou
AISTATS 2021


Preprints/Workshop Papers

Quantitative Bounds for Length Generalization in Transformers
Zachary Izzo, Eshaan Nichani, Jason D. Lee
ICML HiLD & MOSS, COLT FoPT 2025

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach
Haoxuan Chen, Yinuo Ren, Martin Renqiang Min, Lexing Ying, Zachary Izzo
arXiv 2025

Subgroup Discovery with the Cox Model
Zachary Izzo, Iain Melvin
NeurIPS IAI 2024

A Theoretical Study of Dataset Distillation
Zachary Izzo, James Zou
NeurIPS M3L 2023

Importance Tempering: Group Robustness for Overparameterized Models
Yiping Lu, Wenlong Ji, Zachary Izzo, Lexing Ying
arXiv 2022