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Uri Stemmer
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- affiliation: Tel Aviv University, Israel
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2020 – today
- 2024
- [j12]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming Via Differential Privacy and Difference Estimators. Algorithmica 86(11): 3339-3394 (2024) - [c49]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. COLT 2024: 1200-1222 - [c48]Bar Alon
, Moni Naor
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CRYPTO (8) 2024: 74-108 - [c47]Uri Stemmer:
Private Truly-Everlasting Robust-Prediction. ICML 2024 - [i56]Uri Stemmer:
Private Truly-Everlasting Robust-Prediction. CoRR abs/2401.04311 (2024) - [i55]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries. CoRR abs/2403.00028 (2024) - [i54]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 abs/2405.15753 (2024) - [i53]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: 373 (2024) - 2023
- [c46]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: 7235-7243 - [c45]Menachem Sadigurschi, Moshe Shechner, Uri Stemmer:
Relaxed Models for Adversarial Streaming: The Bounded Interruptions Model and the Advice Model. ESA 2023: 91:1-91:14 - [c44]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. EUROCRYPT (3) 2023: 35-65 - [c43]Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer:
Concurrent Shuffle Differential Privacy Under Continual Observation. ICML 2023: 33961-33982 - [c42]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. ITCS 2023: 8:1-8:19 - [c41]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Generalized Private Selection and Testing with High Confidence. ITCS 2023: 39:1-39:23 - [c40]Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer:
Black-Box Differential Privacy for Interactive ML. NeurIPS 2023 - [c39]Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan:
Private Everlasting Prediction. NeurIPS 2023 - [c38]Kobbi Nissim, Uri Stemmer, Eliad Tsfadia:
Adaptive Data Analysis in a Balanced Adversarial Model. NeurIPS 2023 - [c37]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Optimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. STOC 2023: 472-482 - [i52]Menachem Sadigurschi, Moshe Shechner, Uri Stemmer:
Relaxed Models for Adversarial Streaming: The Advice Model and the Bounded Interruptions Model. CoRR abs/2301.09203 (2023) - [i51]Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer:
Concurrent Shuffle Differential Privacy Under Continual Observation. CoRR abs/2301.12535 (2023) - [i50]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. CoRR abs/2302.05707 (2023) - [i49]Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer:
On Differentially Private Online Predictions. CoRR abs/2302.14099 (2023) - [i48]Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan:
Private Everlasting Prediction. CoRR abs/2305.09579 (2023) - [i47]Kobbi Nissim, Uri Stemmer, Eliad Tsfadia:
Adaptive Data Analysis in a Balanced Adversarial Model. CoRR abs/2305.15452 (2023) - [i46]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Hot PATE: Private Aggregation of Distributions for Diverse Task. CoRR abs/2312.02132 (2023) - [i45]Itai Dinur, Uri Stemmer, David P. Woodruff, Samson Zhou:
On Differential Privacy and Adaptive Data Analysis with Bounded Space. IACR Cryptol. ePrint Arch. 2023: 171 (2023) - 2022
- [j11]Avinatan Hassidim
Adversarially Robust Streaming Algorithms via Differential Privacy. J. ACM 69(6): 42:1-42:14 (2022) - [j10]Haim Kaplan
Differentially Private Learning of Geometric Concepts. SIAM J. Comput. 51(4): 952-974 (2022) - [c36]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. COLT 2022: 842-866 - [c35]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer:
On the Robustness of CountSketch to Adaptive Inputs. ICML 2022: 4112-4140 - [c34]Haim Kaplan, Shachar Schnapp, Uri Stemmer:
Differentially Private Approximate Quantiles. ICML 2022: 10751-10761 - [c33]Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer:
Adaptive Data Analysis with Correlated Observations. ICML 2022: 11483-11498 - [c32]Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer:
FriendlyCore: Practical Differentially Private Aggregation. ICML 2022: 21828-21863 - [c31]Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak
Dynamic algorithms against an adaptive adversary: generic constructions and lower bounds. STOC 2022: 1671-1684 - [i44]Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer:
Adaptive Data Analysis with Correlated Observations. CoRR abs/2201.08704 (2022) - [i43]Olivier Bousquet, Amit Daniely, Haim Kaplan, Yishay Mansour, Shay Moran, Uri Stemmer:
Monotone Learning. CoRR abs/2202.05246 (2022) - [i42]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Moshe Shechner, Uri Stemmer:
On the Robustness of CountSketch to Adaptive Inputs. CoRR abs/2202.13736 (2022) - [i41]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 abs/2207.00956 (2022) - [i40]Bar Alon
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably. CoRR abs/2207.05047 (2022) - [i39]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Õptimal Differentially Private Learning of Thresholds and Quasi-Concave Optimization. CoRR abs/2211.06387 (2022) - [i38]Edith Cohen, Xin Lyu, Jelani Nelson, Tamás Sarlós, Uri Stemmer:
Generalized Private Selection and Testing with High Confidence. CoRR abs/2211.12063 (2022) - [i37]Olivier Bousquet, Haim Kaplan, Aryeh Kontorovich, Yishay Mansour, Shay Moran, Menachem Sadigurschi, Uri Stemmer:
Differentially-Private Bayes Consistency. CoRR abs/2212.04216 (2022) - [i36]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: 902 (2022) - 2021
- [j9]Amos Beimel
Learning Privately with Labeled and Unlabeled Examples. Algorithmica 83(1): 177-215 (2021) - [j8]Uri Stemmer:
Locally Private k-Means Clustering. J. Mach. Learn. Res. 22: 176:1-176:30 (2021) - [j7]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) - [c30]Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer:
Differentially Private Weighted Sampling. AISTATS 2021: 2404-2412 - [c29]Haim Kaplan, Yishay Mansour, Uri Stemmer:
The Sparse Vector Technique, Revisited. COLT 2021: 2747-2776 - [c28]Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer:
Separating Adaptive Streaming from Oblivious Streaming Using the Bounded Storage Model. CRYPTO (3) 2021: 94-121 - [c27]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: 1178-1189 - [c26]Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Differentially-Private Clustering of Easy Instances. ICML 2021: 2049-2059 - [c25]Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer:
Differentially Private Multi-Armed Bandits in the Shuffle Model. NeurIPS 2021: 24956-24967 - [c24]Menachem Sadigurschi, Uri Stemmer:
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. NeurIPS 2021: 28286-28297 - [i35]Haim Kaplan, Yishay Mansour, Kobbi Nissim, Uri Stemmer:
Separating Adaptive Streaming from Oblivious Streaming. CoRR abs/2101.10836 (2021) - [i34]Jay Tenenbaum, Haim Kaplan, Yishay Mansour, Uri Stemmer:
Differentially Private Multi-Armed Bandits in the Shuffle Model. CoRR abs/2106.02900 (2021) - [i33]Menachem Sadigurschi, Uri Stemmer:
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles. CoRR abs/2107.11526 (2021) - [i32]Idan Attias, Edith Cohen, Moshe Shechner, Uri Stemmer:
A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR abs/2107.14527 (2021) - [i31]Haim Kaplan, Shachar Schnapp, Uri Stemmer:
Differentially Private Approximate Quantiles. CoRR abs/2110.05429 (2021) - [i30]Eliad Tsfadia, Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer:
FriendlyCore: Practical Differentially Private Aggregation. CoRR abs/2110.10132 (2021) - [i29]Amos Beimel, Haim Kaplan, Yishay Mansour, Kobbi Nissim, Thatchaphol Saranurak
Dynamic Algorithms Against an Adaptive Adversary: Generic Constructions and Lower Bounds. CoRR abs/2111.03980 (2021) - [i28]Haim Kaplan, Uri Stemmer:
A Note on Sanitizing Streams with Differential Privacy. CoRR abs/2111.13762 (2021) - [i27]Edith Cohen, Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Differentially-Private Clustering of Easy Instances. CoRR abs/2112.14445 (2021) - 2020
- [j6]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. J. Mach. Learn. Res. 21: 16:1-16:42 (2020) - [c23]Moshe Shechner, Or Sheffet, Uri Stemmer:
Private k-Means Clustering with Stability Assumptions. AISTATS 2020: 2518-2528 - [c22]Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer:
Closure Properties for Private Classification and Online Prediction. COLT 2020: 119-152 - [c21]Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer:
Privately Learning Thresholds: Closing the Exponential Gap. COLT 2020: 2263-2285 - [c20]Haim Kaplan, Micha Sharir, Uri Stemmer:
How to Find a Point in the Convex Hull Privately. SoCG 2020: 52:1-52:15 - [c19]Amos Beimel
The Power of Synergy in Differential Privacy: Combining a Small Curator with Local Randomizers. ITC 2020: 14:1-14:25 - [c18]Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Adversarially Robust Streaming Algorithms via Differential Privacy. NeurIPS 2020 - [c17]Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. NeurIPS 2020 - [c16]Uri Stemmer:
Locally Private k-Means Clustering. SODA 2020: 548-559 - [c15]Amos Beimel
On the Round Complexity of the Shuffle Model. TCC (2) 2020: 683-712 - [i26]Noga Alon, Amos Beimel, Shay Moran, Uri Stemmer:
Closure Properties for Private Classification and Online Prediction. CoRR abs/2003.04509 (2020) - [i25]Haim Kaplan, Micha Sharir, Uri Stemmer:
How to Find a Point in the Convex Hull Privately. CoRR abs/2003.13192 (2020) - [i24]Avinatan Hassidim, Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Adversarially Robust Streaming Algorithms via Differential Privacy. CoRR abs/2004.05975 (2020) - [i23]Haim Kaplan, Yishay Mansour, Uri Stemmer, Eliad Tsfadia:
Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity. CoRR abs/2004.07839 (2020) - [i22]Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer:
On the Round Complexity of the Shuffle Model. CoRR abs/2009.13510 (2020) - [i21]Haim Kaplan, Yishay Mansour, Uri Stemmer:
The Sparse Vector Technique, Revisited. CoRR abs/2010.00917 (2020) - [i20]Edith Cohen, Ofir Geri, Tamás Sarlós, Uri Stemmer:
Differentially Private Weighted Sampling. CoRR abs/2010.13048 (2020) - [i19]Amos Beimel, Iftach Haitner, Kobbi Nissim, Uri Stemmer:
On the Round Complexity of the Shuffle Model. IACR Cryptol. ePrint Arch. 2020: 1182 (2020)
2010 – 2019
- 2019
- [j5]Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. J. Mach. Learn. Res. 20: 94:1-94:34 (2019) - [j4]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the Sample Complexity of Pure Private Learners. J. Mach. Learn. Res. 20: 146:1-146:33 (2019) - [j3]Uri Stemmer, Kobbi Nissim:
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [j2]Mark Bun, Jelani Nelson, Uri Stemmer:
Heavy Hitters and the Structure of Local Privacy. ACM Trans. Algorithms 15(4): 51:1-51:40 (2019) - [c14]Amos Beimel
Private Center Points and Learning of Halfspaces. COLT 2019: 269-282 - [c13]Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Differentially Private Learning of Geometric Concepts. ICML 2019: 3233-3241 - [i18]Haim Kaplan, Yishay Mansour, Yossi Matias, Uri Stemmer:
Differentially Private Learning of Geometric Concepts. CoRR abs/1902.05017 (2019) - [i17]Amos Beimel, Shay Moran, Kobbi Nissim, Uri Stemmer:
Private Center Points and Learning of Halfspaces. CoRR abs/1902.10731 (2019) - [i16]Uri Stemmer:
Locally Private k-Means Clustering. CoRR abs/1907.02513 (2019) - [i15]Haim Kaplan, Katrina Ligett, Yishay Mansour, Moni Naor, Uri Stemmer:
Privately Learning Thresholds: Closing the Exponential Gap. CoRR abs/1911.10137 (2019) - [i14]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 abs/1912.08951 (2019) - 2018
- [c12]Kobbi Nissim, Uri Stemmer:
Clustering Algorithms for the Centralized and Local Models. ALT 2018: 619-653 - [c11]Uri Stemmer, Haim Kaplan:
Differentially Private k-Means with Constant Multiplicative Error. NeurIPS 2018: 5436-5446 - [c10]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [c9]Mark Bun, Jelani Nelson, Uri Stemmer
Heavy Hitters and the Structure of Local Privacy. PODS 2018: 435-447 - [i13]Haim Kaplan, Uri Stemmer:
Differentially Private k-Means with Constant Multiplicative Error. CoRR abs/1804.08001 (2018) - [i12]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - 2017
- [c8]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Guha Thakurta:
Practical Locally Private Heavy Hitters. NIPS 2017: 2288-2296 - [i11]Kobbi Nissim, Uri Stemmer:
Concentration Bounds for High Sensitivity Functions Through Differential Privacy. CoRR abs/1703.01970 (2017) - [i10]Kobbi Nissim, Uri Stemmer:
Clustering Algorithms for the Centralized and Local Models. CoRR abs/1707.04766 (2017) - [i9]Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta:
Practical Locally Private Heavy Hitters. CoRR abs/1707.04982 (2017) - [i8]Mark Bun, Jelani Nelson, Uri Stemmer:
Heavy Hitters and the Structure of Local Privacy. CoRR abs/1711.04740 (2017) - 2016
- [j1]Amos Beimel
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. Theory Comput. 12(1): 1-61 (2016) - [c7]Mark Bun, Kobbi Nissim
Simultaneous Private Learning of Multiple Concepts. ITCS 2016: 369-380 - [c6]Kobbi Nissim
Locating a Small Cluster Privately. PODS 2016: 413-427 - [c5]Raef Bassily, Kobbi Nissim
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [i7]Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Locating a Small Cluster Privately. CoRR abs/1604.05590 (2016) - 2015
- [c4]Mark Bun, Kobbi Nissim
Differentially Private Release and Learning of Threshold Functions. FOCS 2015: 634-649 - [c3]Amos Beimel, Kobbi Nissim
Learning Privately with Labeled and Unlabeled Examples. SODA 2015: 461-477 - [i6]Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan:
Differentially Private Release and Learning of Threshold Functions. CoRR abs/1504.07553 (2015) - [i5]Raef Bassily, Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
Algorithmic Stability for Adaptive Data Analysis. CoRR abs/1511.02513 (2015) - [i4]Mark Bun, Kobbi Nissim, Uri Stemmer:
Simultaneous Private Learning of Multiple Concepts. CoRR abs/1511.08552 (2015) - 2014
- [i3]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Characterizing the Sample Complexity of Private Learners. CoRR abs/1402.2224 (2014) - [i2]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Learning Privately with Labeled and Unlabeled Examples. CoRR abs/1407.2662 (2014) - [i1]Amos Beimel, Kobbi Nissim, Uri Stemmer:
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. CoRR abs/1407.2674 (2014) - 2013
- [c2]Amos Beimel
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy. APPROX-RANDOM 2013: 363-378 - [c1]Amos Beimel
Characterizing the sample complexity of private learners. ITCS 2013: 97-110
Coauthor Index
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