Characterizing the sample complexity of private learners. - Amos Beimel; Kobbi Nissim; Uri Stemmer (ITCS, 2013)

Differentially Private Release and Learning of Threshold Functions. - Mark Bun; Kobbi Nissim; Uri Stemmer; Salil P. Vadhan (FOCS, 2015)

Locating a Small Cluster Privately. - Kobbi Nissim; Uri Stemmer; Salil P. Vadhan (PODS, 2016)

Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. - Amos Beimel; Kobbi Nissim; Uri Stemmer (Theory Comput., 2016)

Clustering Algorithms for the Centralized and Local Models. - Kobbi Nissim; Uri Stemmer (ALT, 2018)

Differentially Private k-Means with Constant Multiplicative Error. - Uri Stemmer; Haim Kaplan (NeurIPS, 2018)

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

Private Center Points and Learning of Halfspaces. - Amos Beimel; Shay Moran; Kobbi Nissim; Uri Stemmer (COLT, 2019)

Characterizing the Sample Complexity of Pure Private Learners. - Amos Beimel; Kobbi Nissim; Uri Stemmer (J. Mach. Learn. Res., 2019)

Simultaneous Private Learning of Multiple Concepts. - Mark Bun; Kobbi Nissim; Uri Stemmer (J. Mach. Learn. Res., 2019)

Concentration Bounds for High Sensitivity Functions Through Differential Privacy. - Uri Stemmer; Kobbi Nissim (J. Priv. Confidentiality, 2019)

Heavy Hitters and the Structure of Local Privacy. - Mark Bun; Jelani Nelson; Uri Stemmer (ACM Trans. Algorithms, 2019)

Private k-Means Clustering with Stability Assumptions. - Moshe Shechner; Or Sheffet; Uri Stemmer (AISTATS, 2020)

Closure Properties for Private Classification and Online Prediction. - Noga Alon; Amos Beimel; Shay Moran; Uri Stemmer (COLT, 2020)

Privately Learning Thresholds: Closing the Exponential Gap. - Haim Kaplan; Katrina Ligett; Yishay Mansour; Moni Naor; Uri Stemmer (COLT, 2020)

How to Find a Point in the Convex Hull Privately. - Haim Kaplan; Micha Sharir; Uri Stemmer (SoCG, 2020)

The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. - Amos Beimel; Aleksandra Korolova; Kobbi Nissim; Or Sheffet; Uri Stemmer (ITC, 2020)

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. - Haim Kaplan; Yishay Mansour; Uri Stemmer; Eliad Tsfadia (NeurIPS, 2020)

Locally Private k-Means Clustering. - Uri Stemmer (SODA, 2020)

On the Round Complexity of the Shuffle Model. - Amos Beimel; Iftach Haitner; Kobbi Nissim; Uri Stemmer (IACR Cryptol. ePrint Arch., 2020)

Practical Locally Private Heavy Hitters. - Raef Bassily; Kobbi Nissim; Uri Stemmer; Abhradeep Thakurta (J. Mach. Learn. Res., 2020)

Differentially Private Weighted Sampling. - Edith Cohen; Ofir Geri; Tamás Sarlós; Uri Stemmer (AISTATS, 2021)

The Sparse Vector Technique, Revisited. - Haim Kaplan; Yishay Mansour; Uri Stemmer (COLT, 2021)

Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. - Haim Kaplan; Yishay Mansour; Kobbi Nissim; Uri Stemmer (CRYPTO (3), 2021)

Learning and Evaluating a Differentially Private Pre-trained Language Model. - Shlomo Hoory; Amir Feder; Avichai Tendler; Sofia Erell; Alon Peled-Cohen; Itay Laish; Hootan Nakhost; Uri Stemmer; Ayelet Benjamini; Avinatan Hassidim; Yossi Matias (EMNLP (Findings), 2021)

Differentially-Private Clustering of Easy Instances. - Edith Cohen; Haim Kaplan; Yishay Mansour; Uri Stemmer; Eliad Tsfadia (ICML, 2021)

On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. - Menachem Sadigurschi; Uri Stemmer (NeurIPS, 2021)

Differentially Private Multi-Armed Bandits in the Shuffle Model. - Jay Tenenbaum; Haim Kaplan; Yishay Mansour; Uri Stemmer (NeurIPS, 2021)

Learning Privately with Labeled and Unlabeled Examples. - Amos Beimel; Kobbi Nissim; Uri Stemmer (Algorithmica, 2021)

Separating Adaptive Streaming from Oblivious Streaming. - Haim Kaplan; Yishay Mansour; Kobbi Nissim; Uri Stemmer (CoRR, 2021)

A Note on Sanitizing Streams with Differential Privacy. - Haim Kaplan; Uri Stemmer (CoRR, 2021)

Locally Private k-Means Clustering. - Uri Stemmer (J. Mach. Learn. Res., 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)

Monotone Learning. - Olivier Bousquet; Amit Daniely; Haim Kaplan; Yishay Mansour; Shay Moran; Uri Stemmer (COLT, 2022)

On the Robustness of CountSketch to Adaptive Inputs. - Edith Cohen; Xin Lyu 002; Jelani Nelson; Tamás Sarlós; Moshe Shechner; Uri Stemmer (ICML, 2022)

Differentially Private Approximate Quantiles. - Haim Kaplan; Shachar Schnapp; Uri Stemmer (ICML, 2022)

Adaptive Data Analysis with Correlated Observations. - Aryeh Kontorovich; Menachem Sadigurschi; Uri Stemmer (ICML, 2022)

FriendlyCore: Practical Differentially Private Aggregation. - Eliad Tsfadia; Edith Cohen; Haim Kaplan; Yishay Mansour; Uri Stemmer (ICML, 2022)

Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. - Amos Beimel; Haim Kaplan; Yishay Mansour; Kobbi Nissim; Thatchaphol Saranurak; Uri Stemmer (STOC, 2022)

Differentially-Private Bayes Consistency. - Olivier Bousquet; Haim Kaplan; Aryeh Kontorovich; Yishay Mansour; Shay Moran; Menachem Sadigurschi; Uri Stemmer (CoRR, 2022)

MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. - Bar Alon; Moni Naor; Eran Omri; Uri Stemmer (IACR Cryptol. ePrint Arch., 2022)

Adversarially Robust Streaming Algorithms via Differential Privacy. - Avinatan Hassidim; Haim Kaplan; Yishay Mansour; Yossi Matias; Uri Stemmer (J. ACM, 2022)

Differentially Private Learning of Geometric Concepts. - Haim Kaplan; Yishay Mansour; Yossi Matias; Uri Stemmer (SIAM J. Comput., 2022)

Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. - Edith Cohen; Jelani Nelson; Tamás Sarlós; Uri Stemmer (AAAI, 2023)

Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model. - Menachem Sadigurschi; Moshe Shechner; Uri Stemmer (ESA, 2023)

Concurrent Shuffle Differential Privacy Under Continual Observation. - Jay Tenenbaum; Haim Kaplan; Yishay Mansour; Uri Stemmer (ICML, 2023)

Generalized Private Selection and Testing with High Confidence. - Edith Cohen; Xin Lyu 002; Jelani Nelson; Tamás Sarlós; Uri Stemmer (ITCS, 2023)

Black-Box Differential Privacy for Interactive ML. - Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer (NeurIPS, 2023)

Private Everlasting Prediction. - Moni Naor; Kobbi Nissim; Uri Stemmer; Chao Yan (NeurIPS, 2023)

Adaptive Data Analysis in a Balanced Adversarial Model. - Kobbi Nissim; Uri Stemmer; Eliad Tsfadia (NeurIPS, 2023)

Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. - Edith Cohen; Xin Lyu 002; Jelani Nelson; Tamás Sarlós; Uri Stemmer (STOC, 2023)

Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model. - Menachem Sadigurschi; Moshe Shechner; Uri Stemmer (CoRR, 2023)

On Differentially Private Online Predictions. - Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer (CoRR, 2023)

Hot PATE: Private Aggregation of Distributions for Diverse Task. - Edith Cohen; Xin Lyu 002; Jelani Nelson; Tamás Sarlós; Uri Stemmer (CoRR, 2023)

On Differential Privacy and Adaptive Data Analysis with Bounded Space. - Itai Dinur; Uri Stemmer; David P. Woodruff; Samson Zhou (IACR Cryptol. ePrint Arch., 2023)

Private Truly-Everlasting Robust-Prediction. - Uri Stemmer (ICML, 2024)

One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches. - Edith Cohen; Jelani Nelson; Tamás Sarlós; Mihir Singhal; Uri Stemmer (CoRR, 2024)

Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. - Edith Cohen; Xin Lyu 002; Jelani Nelson; Tamás Sarlós; Uri Stemmer (IACR Cryptol. ePrint Arch., 2024)

A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators. - Idan Attias; Edith Cohen; Moshe Shechner; Uri Stemmer (Algorithmica, 2024)

Minimizing Recourse in an Adaptive Balls and Bins Game. - Adi Fine; Haim Kaplan; Uri Stemmer (ICALP, 2025)

Data Reconstruction: When You See It and When You Don't. - Edith Cohen; Haim Kaplan; Yishay Mansour; Shay Moran; Kobbi Nissim; Uri Stemmer; Eliad Tsfadia (ITCS, 2025)

On Differentially Private Linear Algebra. - Haim Kaplan; Yishay Mansour; Shay Moran; Uri Stemmer; Nitzan Tur (STOC, 2025)

Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries. - Edith Cohen; Mihir Singhal; Uri Stemmer (CoRR, 2025)

Nearly Optimal Sample Complexity for Learning with Label Proportions. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Haim Kaplan; Tomer Koren; Uri Stemmer (CoRR, 2025)

Bayesian Perspective on Memorization and Reconstruction. - Haim Kaplan; Yishay Mansour; Kobbi Nissim; Uri Stemmer (CoRR, 2025)

Tight Bounds for Answering Adaptively Chosen Concentrated Queries. - Emma Rapoport; Edith Cohen; Uri Stemmer (CoRR, 2025)

The Cost of Compression: Tight Quadratic Black-Box Attacks on Sketches for ℓ2 Norm Estimation. - Sara Ahmadian; Edith Cohen; Uri Stemmer (CoRR, 2025)