Uri Stemmer

ORCID

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 105

Top Venues

Publications

Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CRYPTO (8) (2024)
Edith Cohen, Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Data Reconstruction: When You See It and When You Don't. CoRR (2024)
Uri Stemmer
Private Truly-Everlasting Robust-Prediction. CoRR (2024)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. COLT (2024)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. CoRR (2024)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. IACR Cryptol. ePrint Arch. 2024 (2024)
Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
On Differentially Private Online Predictions. CoRR (2023)
Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
Private Everlasting Prediction. CoRR (2023)
Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. ITCS (2023)
Menachem Sadigurschi, Moshe Shechner, Uri Stemmer
Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model. CoRR (2023)
Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
On Differential Privacy and Adaptive Data Analysis with Bounded Space. CoRR (2023)
Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer
Black-Box Differential Privacy for Interactive ML. NeurIPS (2023)
Menachem Sadigurschi, Moshe Shechner, Uri Stemmer
Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model. ESA (2023)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. STOC (2023)
Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
On Differential Privacy and Adaptive Data Analysis with Bounded Space. EUROCRYPT (3) (2023)
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Adaptive Data Analysis in a Balanced Adversarial Model. NeurIPS (2023)
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Concurrent Shuffle Differential Privacy Under Continual Observation. CoRR (2023)
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Concurrent Shuffle Differential Privacy Under Continual Observation. ICML (2023)
Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. AAAI (2023)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Generalized Private Selection and Testing with High Confidence. ITCS (2023)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Hot PATE: Private Aggregation of Distributions for Diverse Task. CoRR (2023)
Kobbi Nissim, Uri Stemmer, Eliad Tsfadia
Adaptive Data Analysis in a Balanced Adversarial Model. CoRR (2023)
Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou
On Differential Privacy and Adaptive Data Analysis with Bounded Space. IACR Cryptol. ePrint Arch. 2023 (2023)
Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan
Private Everlasting Prediction. NeurIPS (2023)
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
Adaptive Data Analysis with Correlated Observations. ICML (2022)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. CoRR (2022)
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
Adaptive Data Analysis with Correlated Observations. CoRR (2022)
Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer
Differentially-Private Bayes Consistency. CoRR (2022)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Generalized Private Selection and Testing with High Confidence. CoRR (2022)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. COLT (2022)
Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. IACR Cryptol. ePrint Arch. 2022 (2022)
Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer
Monotone Learning. CoRR (2022)
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Differentially Private Learning of Geometric Concepts. SIAM J. Comput. 51 (4) (2022)
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. STOC (2022)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer
On the Robustness of CountSketch to Adaptive Inputs. CoRR (2022)
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Adversarially Robust Streaming Algorithms via Differential Privacy. J. ACM 69 (6) (2022)
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer
FriendlyCore: Practical Differentially Private Aggregation. ICML (2022)
Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer
On the Robustness of CountSketch to Adaptive Inputs. ICML (2022)
Haim Kaplan, Shachar Schnapp, Uri Stemmer
Differentially Private Approximate Quantiles. ICML (2022)
Bar Alon, Moni Naor, Eran Omri, Uri Stemmer
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CoRR (2022)
Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer
Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs. CoRR (2022)
Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
Algorithmic Stability for Adaptive Data Analysis. SIAM J. Comput. 50 (3) (2021)
Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. CRYPTO (3) (2021)
Menachem Sadigurschi, Uri Stemmer
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. NeurIPS (2021)
Haim Kaplan, Yishay Mansour, Uri Stemmer
The Sparse Vector Technique, Revisited. COLT (2021)
Uri Stemmer
Locally Private k-Means Clustering. J. Mach. Learn. Res. 22 (2021)
Haim Kaplan, Uri Stemmer
A Note on Sanitizing Streams with Differential Privacy. CoRR (2021)
Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds. CoRR (2021)
Haim Kaplan, Shachar Schnapp, Uri Stemmer
Differentially Private Approximate Quantiles. CoRR (2021)
Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer
Separating Adaptive Streaming from Oblivious Streaming. CoRR (2021)
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Differentially Private Multi-Armed Bandits in the Shuffle Model. NeurIPS (2021)
Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR (2021)
Shlomo Hoory, Amir Feder, Avichai Tendler, Sofia Erell, Alon Peled-Cohen, Itay Laish, Hootan Nakhost, Uri Stemmer, Ayelet Benjamini, Avinatan Hassidim, Yossi Matias
Learning and Evaluating a Differentially Private Pre-trained Language Model. EMNLP (Findings) (2021)
Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer
FriendlyCore: Practical Differentially Private Aggregation. CoRR (2021)
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Differentially-Private Clustering of Easy Instances. CoRR (2021)
Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer
Differentially Private Multi-Armed Bandits in the Shuffle Model. CoRR (2021)
Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer
Differentially Private Weighted Sampling. AISTATS (2021)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Learning Privately with Labeled and Unlabeled Examples. Algorithmica 83 (1) (2021)
Menachem Sadigurschi, Uri Stemmer
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. CoRR (2021)
Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Differentially-Private Clustering of Easy Instances. ICML (2021)
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. ITC (2020)
Uri Stemmer
-Means Clustering. SODA (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. TCC (2) (2020)
Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer
Closure Properties for Private Classification and Online Prediction. CoRR (2020)
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Adversarially Robust Streaming Algorithms via Differential Privacy. NeurIPS (2020)
Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Adversarially Robust Streaming Algorithms via Differential Privacy. CoRR (2020)
Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. NeurIPS (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. CoRR (2020)
Moshe Shechner, Or Sheffet, Uri Stemmer
Private k-Means Clustering with Stability Assumptions. AISTATS (2020)
Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer
Differentially Private Weighted Sampling. CoRR (2020)
Haim Kaplan, Micha Sharir, Uri Stemmer
How to Find a Point in the Convex Hull Privately. SoCG (2020)
Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer
Privately Learning Thresholds: Closing the Exponential Gap. COLT (2020)
Haim Kaplan, Micha Sharir, Uri Stemmer
How to Find a Point in the Convex Hull Privately. CoRR (2020)
Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer
Closure Properties for Private Classification and Online Prediction. COLT (2020)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21 (2020)
Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. CoRR (2020)
Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer
On the Round Complexity of the Shuffle Model. IACR Cryptol. ePrint Arch. 2020 (2020)
Haim Kaplan, Yishay Mansour, Uri Stemmer
The Sparse Vector Technique, Revisited. CoRR (2020)
Amos Beimel, Kobbi Nissim, Uri Stemmer
Characterizing the Sample Complexity of Pure Private Learners. J. Mach. Learn. Res. 20 (2019)
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Differentially Private Learning of Geometric Concepts. ICML (2019)
Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
Private Center Points and Learning of Halfspaces. CoRR (2019)
Mark Bun, Kobbi Nissim, Uri Stemmer
Simultaneous Private Learning of Multiple Concepts. J. Mach. Learn. Res. 20 (2019)
Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer
Privately Learning Thresholds: Closing the Exponential Gap. CoRR (2019)
Uri Stemmer, Kobbi Nissim
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. J. Priv. Confidentiality 9 (1) (2019)
Amos Beimel, Aleksandra Korolova, Kobbi Nissim, Or Sheffet, Uri Stemmer
The power of synergy in differential privacy: Combining a small curator with local randomizers. CoRR (2019)
Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer
Private Center Points and Learning of Halfspaces. COLT (2019)
Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer
Differentially Private Learning of Geometric Concepts. CoRR (2019)
Uri Stemmer
Locally Private k-Means Clustering. CoRR (2019)
Mark Bun, Jelani Nelson, Uri Stemmer
Heavy Hitters and the Structure of Local Privacy. ACM Trans. Algorithms 15 (4) (2019)
Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke
The Limits of Post-Selection Generalization. NeurIPS (2018)
Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman
The Limits of Post-Selection Generalization. CoRR (2018)
Kobbi Nissim, Uri Stemmer
Clustering Algorithms for the Centralized and Local Models. ALT (2018)
Haim Kaplan, Uri Stemmer
Differentially Private k-Means with Constant Multiplicative Error. CoRR (2018)
Mark Bun, Jelani Nelson, Uri Stemmer
Heavy Hitters and the Structure of Local Privacy. PODS (2018)
Uri Stemmer, Haim Kaplan
Differentially Private k-Means with Constant Multiplicative Error. NeurIPS (2018)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta
Practical Locally Private Heavy Hitters. CoRR (2017)
Kobbi Nissim, Uri Stemmer
Clustering Algorithms for the Centralized and Local Models. CoRR (2017)
Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta
Practical Locally Private Heavy Hitters. NIPS (2017)
Kobbi Nissim, Uri Stemmer
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. CoRR (2017)
Mark Bun, Jelani Nelson, Uri Stemmer
Heavy Hitters and the Structure of Local Privacy. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Uri Stemmer