Steinke, Thomas

31 publications

ICLR 2025 Near-Exact Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo, Arun Ganesh, Saminul Haque, Thomas Steinke, Abhradeep Guha Thakurta

ICLR 2025 The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Milad Nasr, Thomas Steinke, Borja Balle, Christopher A. Choquette-Choo, Arun Ganesh, Matthew Jagielski, Jamie Hayes, Abhradeep Guha Thakurta, Adam Smith, Andreas Terzis

ICLR 2024 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

ICLR 2024 Privacy Amplification for Matrix Mechanisms Christopher A. Choquette-Choo, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2024 Private Geometric Median Mahdi Haghifam, Thomas Steinke, Jonathan Ullman

ICML 2024 Stealing Part of a Production Language Model Nicholas Carlini, Daniel Paleka, Krishnamurthy Dj Dvijotham, Thomas Steinke, Jonathan Hayase, A. Feder Cooper, Katherine Lee, Matthew Jagielski, Milad Nasr, Arthur Conmy, Eric Wallace, David Rolnick, Florian Tramèr

NeurIPSW 2023 Correlated Noise Provably Beats Independent Noise for Differentially Private Learning Christopher A. Choquette-Choo, Krishnamurthy Dj Dvijotham, Krishna Pillutla, Arun Ganesh, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2023 Counting Distinct Elements Under Person-Level Differential Privacy Thomas Steinke, Alexander Knop

NeurIPS 2023 Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh, Mahdi Haghifam, Thomas Steinke, Abhradeep Guha Thakurta

NeurIPS 2023 Privacy Auditing with One (1) Training Run Thomas Steinke, Milad Nasr, Matthew Jagielski

ICMLW 2023 Privacy Auditing with One (1) Training Run Thomas Steinke, Milad Nasr, Matthew Jagielski

ICML 2023 Why Is Public Pretraining Necessary for Private Model Training? Arun Ganesh, Mahdi Haghifam, Milad Nasr, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang

COLT 2022 A Private and Computationally-Efficient Estimator for Unbounded Gaussians Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan Ullman

ICLR 2022 Hyperparameter Tuning with Renyi Differential Privacy Nicolas Papernot, Thomas Steinke

ICML 2022 Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta

ICML 2022 Public Data-Assisted Mirror Descent for Private Model Training Ehsan Amid, Arun Ganesh, Rajiv Mathews, Swaroop Ramaswamy, Shuang Song, Thomas Steinke, Thomas Steinke, Vinith M Suriyakumar, Om Thakkar, Abhradeep Thakurta

AISTATS 2021 Evading the Curse of Dimensionality in Unconstrained Private GLMs Shuang Song, Thomas Steinke, Om Thakkar, Abhradeep Thakurta

ICML 2021 Leveraging Public Data for Practical Private Query Release Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, Steven Wu

COLT 2021 PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast Rate Bounds That Handle General VC Classes Peter Grunwald, Thomas Steinke, Lydia Zakynthinou

NeurIPS 2021 Privately Learning Subspaces Vikrant Singhal, Thomas Steinke

ICML 2021 The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz, Ziyu Liu, Thomas Steinke

ICML 2020 New Oracle-Efficient Algorithms for Private Synthetic Data Release Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Steven Wu

COLT 2020 Open Problem: Information Complexity of VC Learning Thomas Steinke, Lydia Zakynthinou

COLT 2020 Reasoning About Generalization via Conditional Mutual Information Thomas Steinke, Lydia Zakynthinou

NeurIPS 2020 The Discrete Gaussian for Differential Privacy Clément L Canonne, Gautam Kamath, Thomas Steinke

NeurIPS 2019 Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation Mark Bun, Thomas Steinke

NeurIPS 2019 Private Hypothesis Selection Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu

COLT 2018 Calibrating Noise to Variance in Adaptive Data Analysis Vitaly Feldman, Thomas Steinke

NeurIPS 2018 The Limits of Post-Selection Generalization Jonathan Ullman, Adam Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke

COLT 2017 Generalization for Adaptively-Chosen Estimators via Stable Median Vitaly Feldman, Thomas Steinke

COLT 2015 Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery Thomas Steinke, Jonathan R. Ullman