Ziteng Sun

Research Scientist Google Research, New York Contact: zitengsun at google dot com

About Me

I am a Research Scientist at Google, New York. My research interest lies broadly in machine learning, algorithmic statistics and information theory. I am particularly interested in studying the tradeoffs between different resources in modern data science, including samples, privacy, communication, memory, and computation. I obtained my Ph.D. in Electrical and Computer Engineering at Cornell University, where I was extremely fortunate to be advised by Prof. Jayadev Acharya. Previously, I spent four wonderful years at Tsinghua University, where I got a Bachelor of Science degree in Electronic Engineering.

Publications and Preprints

Authorships are in alphabetical order unless otherwise listed.

• The importance of feature preprocessing for differentially private linear optimization
  Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon
  arXiv:2307.11106, to appear at ICLR 2024 (acceptance rate: 31%)
  

• SpecTr: Fast Speculative Decoding via Optimal Transport
  Ziteng Sun*, Ananda Theertha Suresh*, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu
  arXiv:2310.15141, NeurIPS 2023 (acceptance rate: 26.1%)
  *Equal contribution

• Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints
  Jayadev Acharya, Clément L. Canonne, Ziteng Sun, Himanshu Tyagi
  arXiv:2010.06562, NeurIPS 2023 (acceptance rate: 26.1%)
  

• Subset-Based Instance Optimality in Private Estimation
  Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
  arXiv:2303.01262, ICML 2023 (acceptance rate: 27.8%)
  

• User-level Private Stochastic Convex Optimization with Optimal Rates
  Raef Bassily, Ziteng Sun
  Conference version, ICML 2023 (acceptance rate: 27.8%)
  

• Federated Heavy Hitter Recovery under Linear Sketching
  Adria Gascon, Peter Kairouz, Ziteng Sun, Ananda Theertha Suresh
  arXiv:2307.13347, ICML 2023 (acceptance rate: 27.8%)
  

• Concentration Bounds for Discrete Distribution Estimation in KL Divergence
  Clément L. Canonne, Ziteng Sun, Ananda Theertha Suresh
  arXiv:2302.06869, ISIT 2023
  

• Sample Complexity of Distinguishing Cause from Effect
  Jayadev Acharya, Sourbh Bhadane, Arnab Bhattacharyya, Saravanan Kandasamy, Ziteng Sun
  Conference version, AISTATS 2023 (acceptance rate: 29%)
  

• Discrete Distribution Estimation under User-level Local Differential Privacy
  Jayadev Acharya, Yuhan Liu, Ziteng Sun
  arXiv:2211.03757, AISTATS 2023 (acceptance rate: 29%)
  

• The Role of Interactivity in Structured Estimation
  Jayadev Acharya, Clément L. Canonne, Ziteng Sun, Himanshu Tyagi
  arXiv:2203.06870, COLT 2022
  

• Correlated quantization for distributed mean estimation and optimization
  Ananda Theertha Suresh, Ziteng Sun, Jae Hun Ro, Felix Yu
  arXiv:2203.04925, ICML 2022 (acceptance rate: 19.8%)
  

• Distributed estimation with multiple samples per user: sharp rates and phase transition
  Jayadev Acharya, Clément L. Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi
  Conference version, NeurIPS 2021 (acceptance rate: 26%).
  

• Learning with User-Level Privacy
  Daniel Levy*, Ziteng Sun*, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
  arXiv:2102.11845, NeurIPS 2021 (acceptance rate: 26%).
  *Equal contribution

• Robust Testing and Estimation under Manipulation Attacks
  Jayadev Acharya, Ziteng Sun, Huanyu Zhang
  arXiv:2104.10740, ICML 2021 (acceptance rate: 21.5%)
  

• Interactive Inference under Information Constraints
  Jayadev Acharya, Clément L. Canonne, Yuhan Liu, Ziteng Sun, Himanshu Tyagi
  Journal version at IEEE Transactions on Information Theory
  arXiv:2007.10976. Preliminary version appeared at ISIT 2021
  

• Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints
  Jayadev Acharya, Peter Kairouz, Yuhan Liu, Ziteng Sun
  arXiv:2011.00083, ALT 2021 (acceptance rate: 29.3%)

• Differentially Private Assouad, Fano, and Le Cam
  Jayadev Acharya, Ziteng Sun, Huanyu Zhang
  arXiv:2004.06830, ALT 2021 (acceptance rate: 29.3%)
  Poster presentation at Theory and Practice of Differential Privacy (TPDP 2020)
  

• Inference under information constraints III: Local privacy constraints
  Jayadev Acharya, Clément L. Canonne, Cody Freitag, Ziteng Sun, Himanshu Tyagi
  arXiv:2101.07981
  Journal version at IEEE Journal on Selected Areas in Information Theory
  

• Context-Aware Local Differential Privacy
  Jayadev Acharya, K. A. Bonawitz, Peter Kairouz, Daniel Ramage, Ziteng Sun
  arXiv:1911.00038, ICML 2020 (acceptance rate: 21.8%)
  Poster presentation at Theory and Practice of Differential Privacy (TPDP 2019)
  

• Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit
  Jayadev Acharya, Clément L. Canonne, Yanjun Han, Ziteng Sun, Himanshu Tyagi
  arXiv:1907.08743, COLT 2020 (acceptance rate: 30.7%)
  

• Social Distancing Has Merely Stabilized COVID-19 in the US
  Aaron B. Wagner, Elaine L. Hill, Sean E. Ryan, Ziteng Sun, Grace Deng, Sourbh Bhadane, Victor Hernandez Martinez, Peter Wu, Dongmei Li, Ajay Anand, Jayadev Acharya, David S.Matteson
  medRxiv, Journal version at Stat
  

• Advances and Open Problems in Federated Learning
  with Peter Kairouz, H. Brendan McMahan et al.
  arXiv:1912.04977
  Journal version at Foundations and Trends in Machine Learning
  

• Can You Really Backdoor Federated Learning?
  Ziteng Sun, Peter Kairouz, Ananda Theertha Suresh, H. Brendan McMahan
  arXiv:1911.07963, preprint
  Poster presentation at Workshop on Federated Learning for Data Privacy and Confidentiality
  

• Estimating Entropy of Distributions in Constant Space
  Jayadev Acharya, Sourbh Bhadane, Piotr Indyk, Ziteng Sun
  arXiv:1911.07976, NeurIPS 2019 (acceptance rate: 21.2%).
  

• Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters
  Jayadev Acharya, Ziteng Sun
  arXiv:1905.11888, ICML 2019 (acceptance rate: 22.6%).
  

• Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication
  Jayadev Acharya, Ziteng Sun, Huanyu Zhang
  arXiv:1802.04705, AISTATS 2019 (acceptance rate: 32.4%)
  Talk presentation at Privacy in Machine Learning and Artificial Intelligence (PiMLAI 2018, 15.4% of all submissions)
  Code available here
  

• Differentially Private Testing of Identity and Closeness of Discrete Distributions
  Jayadev Acharya, Ziteng Sun, Huanyu Zhang
  arXiv:1707.05128, Spotlight Presentation at NeurIPS 2018 (4% of all submissions).
  

• INSPECTRE: Privately Estimating the Unseen
  Jayadev Acharya, Gautam Kamath, Ziteng Sun, Huanyu Zhang
  arXiv:1803.00008, talk presentation at ICML 2018 (acceptance rate: 25.1%)
  Journal version at Journal of Privacy and Confidentiality 10 (2).
  Poster presentation at Theory and Practice of Differential Privacy (TPDP 2018)
  Code available here
  

• Improved Bounds on Minimax Risk of Estimating Missing Mass
  Jayadev Acharya, Yelun Bao, Yuheng Kang, Ziteng Sun
  Talk presentation at ISIT 2018 (TPC choice session, 4% of all submissions)
  

Teaching

• ECE 4950 (Spring 2019): Machine Learning and Pattern Recognition (Teaching assistant)
  

• ECE 4200 (Spring 2020): Fundamentals of Machine Learning (Teaching assistant)
  

Reviewing Service

Conferences: ISIT 2018, 2019, 2020, 2021; COLT 2019, 2020, 2021; ICML 2019. 2020, 2021; NeurIPS 2019, 2020; FOCS 2020; ICLR 2020, 2021; ALT 2021; Journals: TIT; TPAMI; JSAIT;