Uri Stemmer

Invited Open Problem: Does Differential Privacy Make PAC Learning Much Harder? COLT 2026 Breaking the Quadratic Barrier: Robust Cardinality Sketches for Adaptive Queries ICML 2025 Nearly Optimal Sample Complexity for Learning with Label Proportions ICML 2025 Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries COLT 2024 Private Truly-Everlasting Robust-Prediction ICML 2024 Adaptive Data Analysis in a Balanced Adversarial Model NeurIPS 2023 Black-Box Differential Privacy for Interactive ML NeurIPS 2023 Concurrent Shuffle Differential Privacy Under Continual Observation ICML 2023 Private Everlasting Prediction NeurIPS 2023 Tricking the Hashing Trick: A Tight Lower Bound on the Robustness of CountSketch to Adaptive Inputs AAAI 2023 Adaptive Data Analysis with Correlated Observations ICML 2022 Differentially Private Approximate Quantiles ICML 2022 FriendlyCore: Practical Differentially Private Aggregation ICML 2022 Monotone Learning COLT 2022 On the Robustness of CountSketch to Adaptive Inputs ICML 2022 Differentially-Private Clustering of Easy Instances ICML 2021 Differentially Private Multi-Armed Bandits in the Shuffle Model NeurIPS 2021 Differentially Private Weighted Sampling AISTATS 2021 Learning and Evaluating a Differentially Private Pre-trained Language Model EMNLP 2021 Learning and Evaluating a Differentially Private Pre-trained Language Model NAACL 2021 Locally Private k-Means Clustering JMLR 2021 On the Sample Complexity of Privately Learning Axis-Aligned Rectangles NeurIPS 2021 The Sparse Vector Technique, Revisited COLT 2021 Adversarially Robust Streaming Algorithms via Differential Privacy NeurIPS 2020 Closure Properties for Private Classification and Online Prediction COLT 2020 Practical Locally Private Heavy Hitters JMLR 2020 Private k-Means Clustering with Stability Assumptions AISTATS 2020 Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NeurIPS 2020 Privately Learning Thresholds: Closing the Exponential Gap COLT 2020 Characterizing the Sample Complexity of Pure Private Learners JMLR 2019 Differentially Private Learning of Geometric Concepts ICML 2019 Private Center Points and Learning of Halfspaces COLT 2019 Simultaneous Private Learning of Multiple Concepts JMLR 2019 Clustering Algorithms for the Centralized and Local Models ALT 2018 Differentially Private k-Means with Constant Multiplicative Error NeurIPS 2018 The Limits of Post-Selection Generalization NeurIPS 2018 Practical Locally Private Heavy Hitters NeurIPS 2017