A Rigorous Extension of the Schönhage-Strassen Integer Multiplication Algorithm Using Complex Interval Arithmetic - Thomas Steinke 002; Raazesh Sainudiin (CCA, 2010)

Learning Hurdles for Sleeping Experts. - Varun Kanade; Thomas Steinke 002 (Electron. Colloquium Comput. Complex., 2011)

Hierarchical Heavy Hitters with the Space Saving Algorithm. - Justin Thaler; Michael Mitzenmacher; Thomas Steinke 002 (ALENEX, 2012)

Pseudorandomness for Permutation Branching Programs Without the Group Theory. - Thomas Steinke 002 (Electron. Colloquium Comput. Complex., 2012)

Pseudorandomness for Regular Branching Programs via Fourier Analysis. - Omer Reingold; Thomas Steinke 002; Salil P. Vadhan (Electron. Colloquium Comput. Complex., 2013)

Weighted Polynomial Approximations: Limits for Learning and Pseudorandomness. - Mark Bun; Thomas Steinke 002 (Electron. Colloquium Comput. Complex., 2014)

Pseudorandomness and Fourier Growth Bounds for Width 3 Branching Programs. - Thomas Steinke 002 (Electron. Colloquium Comput. Complex., 2014)

Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. - Thomas Steinke 002; Jonathan R. Ullman (COLT, 2015)

Robust Traceability from Trace Amounts. - Cynthia Dwork; Adam D. Smith 001; Thomas Steinke 002; Jonathan R. Ullman; Salil P. Vadhan (FOCS, 2015)

Pseudorandomness for Read-Once, Constant-Depth Circuits. - Sitan Chen; Thomas Steinke 002; Salil P. Vadhan (CoRR, 2015)

Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. - Mark Bun; Thomas Steinke 002 (IACR Cryptol. ePrint Arch., 2016)

Between Pure and Approximate Differential Privacy. - Thomas Steinke 002; Jonathan R. Ullman (J. Priv. Confidentiality, 2016)

Generalization for Adaptively-chosen Estimators via Stable Median. - Vitaly Feldman; Thomas Steinke 002 (COLT, 2017)

Tight Lower Bounds for Differentially Private Selection. - Thomas Steinke 002; Jonathan R. Ullman (FOCS, 2017)

Subgaussian Tail Bounds via Stability Arguments. - Thomas Steinke 002; Jonathan R. Ullman (CoRR, 2017)

Pseudorandomness and Fourier-Growth Bounds for Width-3 Branching Programs. - Thomas Steinke 002; Salil P. Vadhan; Andrew Wan (Theory Comput., 2017)

Calibrating Noise to Variance in Adaptive Data Analysis. - Vitaly Feldman; Thomas Steinke 002 (COLT, 2018)

The Limits of Post-Selection Generalization. - Jonathan R. Ullman; Adam D. Smith 001; Kobbi Nissim; Uri Stemmer; Thomas Steinke 002 (NeurIPS, 2018)

Composable and versatile privacy via truncated CDP. - Mark Bun; Cynthia Dwork; Guy N. Rothblum; Thomas Steinke 002 (STOC, 2018)

A Hybrid Approach to Privacy-Preserving Federated Learning. - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028 (CoRR, 2018)

A Hybrid Approach to Privacy-Preserving Federated Learning. - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (AISec@CCS, 2019)

Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation. - Mark Bun; Thomas Steinke 002 (NeurIPS, 2019)

Towards Instance-Optimal Private Query Release. - Jaroslaw Blasiok; Mark Bun; Aleksandar Nikolov; Thomas Steinke 002 (SODA, 2019)

A Hybrid Approach to Privacy-Preserving Federated Learning - (Extended Abstract). - Stacey Truex; Nathalie Baracaldo; Ali Anwar 001; Thomas Steinke 002; Heiko Ludwig; Rui Zhang 028; Yi Zhou 015 (Inform. Spektrum, 2019)

Make Up Your Mind: The Price of Online Queries in Differential Privacy. - Mark Bun; Thomas Steinke 002; Jonathan R. Ullman (J. Priv. Confidentiality, 2019)

Reasoning About Generalization via Conditional Mutual Information. - Thomas Steinke 002; Lydia Zakynthinou (COLT, 2020)

Open Problem: Information Complexity of VC Learning. - Thomas Steinke 002; Lydia Zakynthinou (COLT, 2020)

New Oracle-Efficient Algorithms for Private Synthetic Data Release. - Giuseppe Vietri; Grace Tian; Mark Bun; Thomas Steinke 002; Zhiwei Steven Wu (ICML, 2020)

Multi-Central Differential Privacy. - Thomas Steinke 002 (CoRR, 2020)

Evading the Curse of Dimensionality in Unconstrained Private GLMs. - Shuang Song 001; Thomas Steinke 002; Om Thakkar 001; Abhradeep Thakurta (AISTATS, 2021)

PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes. - Peter Grünwald; Thomas Steinke 002; Lydia Zakynthinou (COLT, 2021)

The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. - Peter Kairouz; Ziyu Liu 002; Thomas Steinke 002 (ICML, 2021)

Leveraging Public Data for Practical Private Query Release. - Terrance Liu; Giuseppe Vietri; Thomas Steinke 002; Jonathan R. Ullman; Zhiwei Steven Wu (ICML, 2021)

Privately Learning Subspaces. - Vikrant Singhal; Thomas Steinke 002 (NeurIPS, 2021)

The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise. - Zeyu Ding 001; Daniel Kifer; Sayed M. Saghaian N. E.; Thomas Steinke 002; Yuxin Wang 004; Yingtai Xiao; Danfeng Zhang (CoRR, 2021)

Algorithmic Stability for Adaptive Data Analysis. - Raef Bassily; Kobbi Nissim; Adam D. Smith 001; Thomas Steinke 002; Uri Stemmer; Jonathan R. Ullman (SIAM J. Comput., 2021)

Private Hypothesis Selection. - Mark Bun; Gautam Kamath 001; Thomas Steinke 002; Zhiwei Steven Wu (IEEE Trans. Inf. Theory, 2021)

A Private and Computationally-Efficient Estimator for Unbounded Gaussians. - Gautam Kamath 001; Argyris Mouzakis; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (COLT, 2022)

Hyperparameter Tuning with Renyi Differential Privacy. - Nicolas Papernot; Thomas Steinke 002 (ICLR, 2022)

Public Data-Assisted Mirror Descent for Private Model Training. - Ehsan Amid; Arun Ganesh; Rajiv Mathews; Swaroop Ramaswamy; Shuang Song 001; Thomas Steinke 002; Vinith M. Suriyakumar; Om Thakkar 001; Abhradeep Thakurta (ICML, 2022)

Debugging Differential Privacy: A Case Study for Privacy Auditing. - Florian Tramèr; Andreas Terzis; Thomas Steinke 002; Shuang Song 001; Matthew Jagielski; Nicholas Carlini (CoRR, 2022)

Composition of Differential Privacy & Privacy Amplification by Subsampling. - Thomas Steinke 002 (CoRR, 2022)

Discrete Gaussian for Differential Privacy. - Clément L. Canonne; Gautam Kamath 001; Thomas Steinke 002 (J. Priv. Confidentiality, 2022)

Why Is Public Pretraining Necessary for Private Model Training? - Arun Ganesh; Mahdi Haghifam; Milad Nasr; Sewoong Oh; Thomas Steinke 002; Om Thakkar 001; Abhradeep Guha Thakurta; Lun Wang 001 (ICML, 2023)

Algorithms with More Granular Differential Privacy Guarantees. - Badih Ghazi; Ravi Kumar 001; Pasin Manurangsi; Thomas Steinke 002 (ITCS, 2023)

Counting Distinct Elements Under Person-Level Differential Privacy. - Thomas Steinke 002; Alexander Knop (NeurIPS, 2023)

Privacy Auditing with One (1) Training Run. - Thomas Steinke 002; Milad Nasr; Matthew Jagielski (NeurIPS, 2023)

Faster Differentially Private Convex Optimization via Second-Order Methods. - Arun Ganesh; Mahdi Haghifam; Thomas Steinke 002; Abhradeep Guha Thakurta (NeurIPS, 2023)

Tight Auditing of Differentially Private Machine Learning. - Milad Nasr; Jamie Hayes; Thomas Steinke 002; Borja Balle; Florian Tramèr; Matthew Jagielski; Nicholas Carlini; Andreas Terzis (USENIX Security Symposium, 2023)

A Bias-Variance-Privacy Trilemma for Statistical Estimation. - Gautam Kamath 001; Argyris Mouzakis; Matthew Regehr; Vikrant Singhal; Thomas Steinke 002; Jonathan R. Ullman (CoRR, 2023)

Differentially Private Stream Processing at Scale. - Bing Zhang; Vadym Doroshenko; Peter Kairouz; Thomas Steinke 002; Abhradeep Thakurta; Ziyin Ma; Himani Apte; Jodi Spacek (CoRR, 2023)

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. - Christopher A. Choquette-Choo; Krishnamurthy Dj Dvijotham; Krishna Pillutla; Arun Ganesh; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2024)

Privacy Amplification for Matrix Mechanisms. - Christopher A. Choquette-Choo; Arun Ganesh; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2024)

Stealing part of a production language model. - Nicholas Carlini; Daniel Paleka; Krishnamurthy Dj Dvijotham; Thomas Steinke 002; Jonathan Hayase; A. Feder Cooper; Katherine Lee; Matthew Jagielski; Milad Nasr; Arthur Conmy; Eric Wallace; David Rolnick; et al. (ICML, 2024)

Differentially Private Medians and Interior Points for Non-Pathological Data. - Maryam Aliakbarpour; Rose Silver; Thomas Steinke 002; Jonathan R. Ullman (ITCS, 2024)

Private Geometric Median. - Mahdi Haghifam; Thomas Steinke 002; Jonathan R. Ullman (NeurIPS, 2024)

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy. - Krishnamurthy Dvijotham; H. Brendan McMahan; Krishna Pillutla; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2024)

Hash-Prune-Invert: Improved Differentially Private Heavy-Hitter Detection in the Two-Server Model. - Borja Balle; James Bell 001; Albert Cheu; Adrià Gascón; Jonathan Katz; Mariana Raykova 001; Phillipp Schoppmann; Thomas Steinke 002 (IACR Cryptol. ePrint Arch., 2024)

Differentially Private Stream Processing at Scale. - Bing Zhang; Vadym Doroshenko; Peter Kairouz; Thomas Steinke 002; Abhradeep Thakurta; Ziyin Ma; Eidan Cohen; Himani Apte; Jodi Spacek (Proc. VLDB Endow., 2024)

Near-Exact Privacy Amplification for Matrix Mechanisms. - Christopher A. Choquette-Choo; Arun Ganesh; Saminul Haque; Thomas Steinke 002; Abhradeep Guha Thakurta (ICLR, 2025)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD. - Milad Nasr; Thomas Steinke 002; Borja Balle; Christopher A. Choquette-Choo; Arun Ganesh; Matthew Jagielski; Jamie Hayes; Abhradeep Guha Thakurta; Adam Smith 006; Andreas Terzis (ICLR, 2025)

Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms Under Composition. - Christian Janos Lebeda; Matthew Regehr; Gautam Kamath 001; Thomas Steinke 002 (SaTML, 2025)

Privately Evaluating Untrusted Black-Box Functions. - Ephraim Linder; Sofya Raskhodnikova; Adam Smith 006; Thomas Steinke 002 (STOC, 2025)

Hush! Protecting Secrets During Model Training: An Indistinguishability Approach. - Arun Ganesh; Brendan McMahan; Milad Nasr; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)

Correlated Noise Mechanisms for Differentially Private Learning. - Krishna Pillutla; Jalaj Upadhyay; Christopher A. Choquette-Choo; Krishnamurthy Dvijotham; Arun Ganesh; Monika Henzinger; Jonathan Katz; Ryan McKenna; H. Brendan McMahan; Keith Rush; Thomas Steinke 002; Abhradeep Thakurta (CoRR, 2025)