| 2026 | COLT | Invited Open Problem: Does Differential Privacy Make PAC Learning Much Harder? | Kobbi Nissim, Uri Stemmer, Eliad Tsfadia |
| 2026 | SODA | One Attack to Rule Them All: Tight Quadratic Bounds for Adaptive Queries on Cardinality Sketches. | Edith Cohen, Jelani Nelson, Tams Sarls, Mihir Singhal, Uri Stemmer |
| 2025 | ICALP | Minimizing Recourse in an Adaptive Balls and Bins Game. | Adi Fine, Haim Kaplan, Uri Stemmer |
| 2025 | ICML | Nearly Optimal Sample Complexity for Learning with Label Proportions. | Rbert Istvan Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer |
| 2025 | ICML | Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries. | Edith Cohen, Mihir Singhal, Uri Stemmer |
| 2025 | STOC | On Differentially Private Linear Algebra. | Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur |
| 2024 | COLT | Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. | Edith Cohen, Xin Lyu, Jelani Nelson, Tams Sarls, Uri Stemmer |
| 2024 | CRYPTO | MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. | Bar Alon, Moni Naor, Eran Omri, Uri Stemmer |
| 2024 | ICML | Private Truly-Everlasting Robust-Prediction. | Uri Stemmer |
| 2023 | AAAI | Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. | Edith Cohen, Jelani Nelson, Tams Sarls, Uri Stemmer |
| 2023 | ESA | Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model. | Menachem Sadigurschi, Moshe Shechner, Uri Stemmer |
| 2023 | EuroCrypt | On Differential Privacy and Adaptive Data Analysis with Bounded Space. | Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou |
| 2023 | ICML | Concurrent Shuffle Differential Privacy Under Continual Observation. | Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2023 | STOC | Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. | Edith Cohen, Xin Lyu, Jelani Nelson, Tams Sarls, Uri Stemmer |
| 2022 | COLT | Monotone Learning. | Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer |
| 2022 | ICML | On the Robustness of CountSketch to Adaptive Inputs. | Edith Cohen, Xin Lyu, Jelani Nelson, Tams Sarls, Moshe Shechner, Uri Stemmer |
| 2022 | ICML | Differentially Private Approximate Quantiles. | Haim Kaplan, Shachar Schnapp, Uri Stemmer |
| 2022 | ICML | Adaptive Data Analysis with Correlated Observations. | Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer |
| 2022 | ICML | FriendlyCore: Practical Differentially Private Aggregation. | Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2022 | STOC | Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. | Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer |
| 2021 | AISTATS | Differentially Private Weighted Sampling. | Edith Cohen, Ofir Geri, Tams Sarls, Uri Stemmer |
| 2021 | COLT | The Sparse Vector Technique, Revisited. | Haim Kaplan, Yishay Mansour, Uri Stemmer |
| 2021 | CRYPTO | Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. | Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer |
| 2021 | EMNLP | 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 |
| 2021 | ICML | Differentially-Private Clustering of Easy Instances. | Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia |
| 2020 | AISTATS | Private k-Means Clustering with Stability Assumptions. | Moshe Shechner, Or Sheffet, Uri Stemmer |
| 2020 | COLT | Closure Properties for Private Classification and Online Prediction. | Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer |
| 2020 | COLT | Privately Learning Thresholds: Closing the Exponential Gap. | Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer |
| 2020 | SODA | Locally Private | Uri Stemmer |
| 2020 | TCC | On the Round Complexity of the Shuffle Model. | Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer |
| 2019 | COLT | Private Center Points and Learning of Halfspaces. | Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer |
| 2019 | ICML | Differentially Private Learning of Geometric Concepts. | Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer |
| 2018 | ALT | Clustering Algorithms for the Centralized and Local Models. | Kobbi Nissim, Uri Stemmer |
| 2018 | PODS | Heavy Hitters and the Structure of Local Privacy. | Mark Bun, Jelani Nelson, Uri Stemmer |
| 2016 | PODS | Locating a Small Cluster Privately. | Kobbi Nissim, Uri Stemmer, Salil P. Vadhan |
| 2016 | STOC | Algorithmic stability for adaptive data analysis. | Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman |
| 2015 | FOCS | Differentially Private Release and Learning of Threshold Functions. | Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan |
| 2015 | SODA | Learning Privately with Labeled and Unlabeled Examples. | Amos Beimel, Kobbi Nissim, Uri Stemmer |