Uri Stemmer

According to our database, Uri Stemmer authored at least 68 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of two.

2025

A Simple and Robust Protocol for Distributed Counting.

Edith Cohen, Moshe Shechner, Uri Stemmer

CoRR, September, 2025

The Cost of Compression: Tight Quadratic Black-Box Attacks on Sketches for ℓ₂ Norm Estimation.

Sara Ahmadian, Edith Cohen, Uri Stemmer

CoRR, July, 2025

Tight Bounds for Answering Adaptively Chosen Concentrated Queries.

Emma Rapoport, Edith Cohen, Uri Stemmer

CoRR, July, 2025

Bayesian Perspective on Memorization and Reconstruction.

Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer

CoRR, May, 2025

Nearly Optimal Sample Complexity for Learning with Label Proportions.

Róbert Busa-Fekete, Travis Dick, Claudio Gentile, Haim Kaplan, Tomer Koren, Uri Stemmer

CoRR, May, 2025

Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries.

Edith Cohen, Mihir Singhal, Uri Stemmer

CoRR, February, 2025

On Differentially Private Linear Algebra.

Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer, Nitzan Tur

Proceedings of the 57th Annual ACM Symposium on Theory of Computing, 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

Proceedings of the 16th Innovations in Theoretical Computer Science Conference, 2025

Minimizing Recourse in an Adaptive Balls and Bins Game.

Adi Fine, Haim Kaplan, Uri Stemmer

Proceedings of the 52nd International Colloquium on Automata, Languages, and Programming, 2025

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

Private Truly-Everlasting Robust-Prediction.

Uri Stemmer

Proceedings of the Forty-first International Conference on Machine Learning, 2024

MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably.

Bar Alon, Moni Naor, Eran Omri, Uri Stemmer

Proceedings of the Advances in Cryptology - CRYPTO 2024, 2024

Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries.

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Hot PATE: Private Aggregation of Distributions for Diverse Task.

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer

CoRR, 2023

On Differentially Private Online Predictions.

Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

CoRR, 2023

Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model.

Menachem Sadigurschi, Moshe Shechner, Uri Stemmer

CoRR, 2023

Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization.

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer

Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Adaptive Data Analysis in a Balanced Adversarial Model.

Kobbi Nissim, Uri Stemmer, Eliad Tsfadia

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Private Everlasting Prediction.

Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Black-Box Differential Privacy for Interactive ML.

Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Generalized Private Selection and Testing with High Confidence.

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators.

Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer

Proceedings of the 14th Innovations in Theoretical Computer Science Conference, 2023

Concurrent Shuffle Differential Privacy Under Continual Observation.

Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2023

On Differential Privacy and Adaptive Data Analysis with Bounded Space.

Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou

Proceedings of the Advances in Cryptology - EUROCRYPT 2023, 2023

Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model.

Menachem Sadigurschi, Moshe Shechner, Uri Stemmer

Proceedings of the 31st Annual European Symposium on Algorithms, 2023

Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs.

Edith Cohen, Jelani Nelson, Tamás Sarlós, Uri Stemmer

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Differentially-Private Bayes Consistency.

Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer

CoRR, 2022

Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds.

Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak, Uri Stemmer

Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

FriendlyCore: Practical Differentially Private Aggregation.

Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2022

Adaptive Data Analysis with Correlated Observations.

Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2022

Differentially Private Approximate Quantiles.

Haim Kaplan, Shachar Schnapp, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2022

On the Robustness of CountSketch to Adaptive Inputs.

Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer

Proceedings of the International Conference on Machine Learning, 2022

Monotone Learning.

Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Locally Private k-Means Clustering.

Uri Stemmer

J. Mach. Learn. Res., 2021

A Note on Sanitizing Streams with Differential Privacy.

Haim Kaplan, Uri Stemmer

CoRR, 2021

Separating Adaptive Streaming from Oblivious Streaming.

Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer

CoRR, 2021

Differentially Private Multi-Armed Bandits in the Shuffle Model.

Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Sample Complexity of Privately Learning Axis-Aligned Rectangles.

Menachem Sadigurschi, Uri Stemmer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Differentially-Private Clustering of Easy Instances.

Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Proceedings of the 38th International Conference on Machine Learning, 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

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model.

Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer

Proceedings of the Advances in Cryptology - CRYPTO 2021, 2021

The Sparse Vector Technique, Revisited.

Haim Kaplan, Yishay Mansour, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2021

Differentially Private Weighted Sampling.

Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

On the Round Complexity of the Shuffle Model.

Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer

Proceedings of the Theory of Cryptography - 18th International Conference, 2020

Locally Private <i>k</i>-Means Clustering.

Uri Stemmer

Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity.

Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Adversarially Robust Streaming Algorithms via Differential Privacy.

Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Proceedings of the 1st Conference on Information-Theoretic Cryptography, 2020

How to Find a Point in the Convex Hull Privately.

Haim Kaplan, Micha Sharir, Uri Stemmer

Proceedings of the 36th International Symposium on Computational Geometry, 2020

Privately Learning Thresholds: Closing the Exponential Gap.

Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2020

Closure Properties for Private Classification and Online Prediction.

Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2020

Private k-Means Clustering with Stability Assumptions.

Moshe Shechner, Or Sheffet, Uri Stemmer

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Concentration Bounds for High Sensitivity Functions Through Differential Privacy.

Uri Stemmer, Kobbi Nissim

J. Priv. Confidentiality, 2019

Characterizing the Sample Complexity of Pure Private Learners.

Amos Beimel, Kobbi Nissim, Uri Stemmer

J. Mach. Learn. Res., 2019

Differentially Private Learning of Geometric Concepts.

Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer

Proceedings of the 36th International Conference on Machine Learning, 2019

Private Center Points and Learning of Halfspaces.

Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer

Proceedings of the Conference on Learning Theory, 2019

2018

Heavy Hitters and the Structure of Local Privacy.

Mark Bun, Jelani Nelson, Uri Stemmer

Proceedings of the 37th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2018

The Limits of Post-Selection Generalization.

Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentially Private k-Means with Constant Multiplicative Error.

Uri Stemmer, Haim Kaplan

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Clustering Algorithms for the Centralized and Local Models.

Kobbi Nissim, Uri Stemmer

Proceedings of the Algorithmic Learning Theory, 2018

2017

Practical Locally Private Heavy Hitters.

Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Algorithmic stability for adaptive data analysis.

Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman

Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, 2016

Locating a Small Cluster Privately.

Kobbi Nissim, Uri Stemmer, Salil P. Vadhan

Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 2016

Simultaneous Private Learning of Multiple Concepts.

Mark Bun, Kobbi Nissim, Uri Stemmer

Proceedings of the 2016 ACM Conference on Innovations in Theoretical Computer Science, 2016

2015

Learning Privately with Labeled and Unlabeled Examples.

Amos Beimel, Kobbi Nissim, Uri Stemmer

Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms, 2015

Differentially Private Release and Learning of Threshold Functions.

Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan

Proceedings of the IEEE 56th Annual Symposium on Foundations of Computer Science, 2015

2013

Characterizing the sample complexity of private learners.

Amos Beimel, Kobbi Nissim, Uri Stemmer

Proceedings of the Innovations in Theoretical Computer Science, 2013

Private Learning and Sanitization: Pure vs. Approximate Differential Privacy.

Amos Beimel, Kobbi Nissim, Uri Stemmer

Proceedings of the Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 2013