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Jonathan R. Ullman
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2020 – today
- 2024
- [j24]Jiawen Liu
, Weihao Qu
Program Analysis for Adaptive Data Analysis. Proc. ACM Program. Lang. 8(PLDI): 914-938 (2024) - [j23]John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. Proc. Priv. Enhancing Technol. 2024(3): 202-223 (2024) - [j22]Liudas Panavas
Investigating the Visual Utility of Differentially Private Scatterplots. IEEE Trans. Vis. Comput. Graph. 30(8): 5370-5385 (2024) - [c64]Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. COLT 2024: 46-93 - [c63]Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. COLT 2024: 4207-4239 - [c62]Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. ICLR 2024 - [c61]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. ICML 2024 - [c60]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS 2024: 3:1-3:21 - [i70]Andrew Lowy, Jonathan R. Ullman, Stephen J. Wright:
How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization. CoRR abs/2402.11173 (2024) - [i69]Sushant Agarwal, Gautam Kamath, Mahbod Majid, Argyris Mouzakis, Rose Silver, Jonathan R. Ullman:
Private Mean Estimation with Person-Level Differential Privacy. CoRR abs/2405.20405 (2024) - [i68]Mahdi Haghifam, Thomas Steinke, Jonathan R. Ullman:
Private Geometric Median. CoRR abs/2406.07407 (2024) - 2023
- [j21]Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models. Proc. Priv. Enhancing Technol. 2023(3): 211-232 (2023) - [j20]Konstantina Bairaktari, Paul Langton, Huy L. Nguyen, Niklas Smedemark-Margulies, Jonathan R. Ullman:
Fair and Useful Cohort Selection. Trans. Mach. Learn. Res. 2023 (2023) - [c59]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou
Multitask Learning via Shared Features: Algorithms and Hardness. COLT 2023: 747-772 - [c58]Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou
From Robustness to Privacy and Back. ICML 2023: 1121-1146 - [c57]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. SP 2023: 400-417 - [i67]Gautam Kamath
A Bias-Variance-Privacy Trilemma for Statistical Estimation. CoRR abs/2301.13334 (2023) - [i66]Hilal Asi, Jonathan R. Ullman, Lydia Zakynthinou:
From Robustness to Privacy and Back. CoRR abs/2302.01855 (2023) - [i65]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR abs/2305.13440 (2023) - [i64]John Abascal, Stanley Wu, Alina Oprea, Jonathan R. Ullman:
TMI! Finetuned Models Leak Private Information from their Pretraining Data. CoRR abs/2306.01181 (2023) - [i63]Naty Peter, Eliad Tsfadia, Jonathan R. Ullman:
Smooth Lower Bounds for Differentially Private Algorithms via Padding-and-Permuting Fingerprinting Codes. CoRR abs/2307.07604 (2023) - [i62]Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan R. Ullman:
Chameleon: Increasing Label-Only Membership Leakage with Adaptive Poisoning. CoRR abs/2310.03838 (2023) - [i61]Maryam Aliakbarpour
Metalearning with Very Few Samples Per Task. CoRR abs/2312.13978 (2023) - 2022
- [c56]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. COLT 2022: 544-572 - [i60]Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan R. Ullman, Roxana Geambasu:
How to Combine Membership-Inference Attacks on Multiple Updated Models. CoRR abs/2205.06369 (2022) - [i59]Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan R. Ullman:
SNAP: Efficient Extraction of Private Properties with Poisoning. CoRR abs/2208.12348 (2022) - [i58]Konstantina Bairaktari, Guy Blanc, Li-Yang Tan, Jonathan R. Ullman, Lydia Zakynthinou:
Multitask Learning via Shared Features: Algorithms and Hardness. CoRR abs/2209.03112 (2022) - [i57]Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
Instance-Optimal Differentially Private Estimation. CoRR abs/2210.15819 (2022) - 2021
- [j19]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - [j18]Adam Sealfon
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. J. Priv. Confidentiality 11(1) (2021) - [j17]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) - [c55]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu
Leveraging Public Data for Practical Private Query Release. ICML 2021: 6968-6977 - [c54]Gavin Brown, Marco Gaboardi
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. NeurIPS 2021: 7950-7964 - [c53]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. SP 2021: 883-900 - [c52]Albert Cheu, Jonathan R. Ullman:
The limits of pan privacy and shuffle privacy for learning and estimation. STOC 2021: 1081-1094 - [i56]Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan R. Ullman, Zhiwei Steven Wu:
Leveraging Public Data for Practical Private Query Release. CoRR abs/2102.08598 (2021) - [i55]Gavin Brown, Marco Gaboardi, Adam D. Smith, Jonathan R. Ullman, Lydia Zakynthinou:
Covariance-Aware Private Mean Estimation Without Private Covariance Estimation. CoRR abs/2106.13329 (2021) - [i54]Gautam Kamath, Argyris Mouzakis, Vikrant Singhal, Thomas Steinke, Jonathan R. Ullman:
A Private and Computationally-Efficient Estimator for Unbounded Gaussians. CoRR abs/2111.04609 (2021) - 2020
- [j16]Jonathan R. Ullman, Salil P. Vadhan:
PCPs and the Hardness of Generating Synthetic Data. J. Cryptol. 33(4): 2078-2112 (2020) - [j15]Matthew Joseph, Aaron Roth
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10(1) (2020) - [j14]Aaron Roth
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8(1): 6:1-6:35 (2020) - [c51]Huy Le Nguyen, Jonathan R. Ullman, Lydia Zakynthinou:
Efficient Private Algorithms for Learning Large-Margin Halfspaces. ALT 2020: 704-724 - [c50]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. COLT 2020: 2204-2235 - [c49]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu
Private Query Release Assisted by Public Data. ICML 2020: 695-703 - [c48]Gautam Kamath
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. ITA 2020: 1-62 - [c47]Sourav Biswas, Yihe Dong, Gautam Kamath, Jonathan R. Ullman:
CoinPress: Practical Private Mean and Covariance Estimation. NeurIPS 2020 - [c46]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou:
Private Identity Testing for High-Dimensional Distributions. NeurIPS 2020 - [c45]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? NeurIPS 2020 - [c44]Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman:
The power of factorization mechanisms in local and central differential privacy. STOC 2020: 425-438 - [i53]Gautam Kamath, Vikrant Singhal, Jonathan R. Ullman:
Private Mean Estimation of Heavy-Tailed Distributions. CoRR abs/2002.09464 (2020) - [i52]Raef Bassily, Albert Cheu, Shay Moran, Aleksandar Nikolov, Jonathan R. Ullman, Zhiwei Steven Wu:
Private Query Release Assisted by Public Data. CoRR abs/2004.10941 (2020) - [i51]Gautam Kamath, Jonathan R. Ullman:
A Primer on Private Statistics. CoRR abs/2005.00010 (2020) - [i50]Sourav Biswas, Yihe Dong
CoinPress: Practical Private Mean and Covariance Estimation. CoRR abs/2006.06618 (2020) - [i49]Matthew Jagielski, Jonathan R. Ullman, Alina Oprea:
Auditing Differentially Private Machine Learning: How Private is Private SGD? CoRR abs/2006.07709 (2020) - [i48]Albert Cheu, Jonathan R. Ullman:
The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation. CoRR abs/2009.08000 (2020)
2010 – 2019
- 2019
- [j13]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. J. Priv. Confidentiality 9(1) (2019) - [j12]Jonathan R. Ullman, Lars Vilhuber
Editorial for Volume 9 Issue 2. J. Priv. Confidentiality 9(2) (2019) - [j11]Jonathan R. Ullman, Lars Vilhuber
Program for TPDP 2017. J. Priv. Confidentiality 9(2) (2019) - [c43]Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman:
Securely Sampling Biased Coins with Applications to Differential Privacy. CCS 2019: 603-614 - [c42]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. COLT 2019: 1853-1902 - [c41]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. EUROCRYPT (1) 2019: 375-403 - [c40]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c39]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. NeurIPS 2019: 168-180 - [c38]Jonathan R. Ullman, Adam Sealfon:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. NeurIPS 2019: 3765-3775 - [c37]Clément L. Canonne, Gautam Kamath
The structure of optimal private tests for simple hypotheses. STOC 2019: 310-321 - [i47]Huy L. Nguyen, Jonathan R. Ullman, Lydia Zakynthinou
Efficient Private Algorithms for Learning Halfspaces. CoRR abs/1902.09009 (2019) - [i46]Adam Sealfon, Jonathan R. Ullman:
Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy. CoRR abs/1905.10477 (2019) - [i45]Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan R. Ullman, Lydia Zakynthinou
Private Identity Testing for High-Dimensional Distributions. CoRR abs/1905.11947 (2019) - [i44]Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan R. Ullman:
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians. CoRR abs/1909.03951 (2019) - [i43]Albert Cheu, Adam D. Smith, Jonathan R. Ullman:
Manipulation Attacks in Local Differential Privacy. CoRR abs/1909.09630 (2019) - [i42]Alexander Edmonds, Aleksandar Nikolov, Jonathan R. Ullman:
The Power of Factorization Mechanisms in Local and Central Differential Privacy. CoRR abs/1911.08339 (2019) - [i41]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Shuffling. IACR Cryptol. ePrint Arch. 2019: 245 (2019) - [i40]Jeffrey Champion, Abhi Shelat, Jonathan R. Ullman:
Securely Sampling Biased Coins with Applications to Differential Privacy. IACR Cryptol. ePrint Arch. 2019: 823 (2019) - 2018
- [j10]Foto N. Afrati, Shantanu Sharma
Computing marginals using MapReduce. J. Comput. Syst. Sci. 94: 98-117 (2018) - [j9]Cynthia Dwork, Jonathan R. Ullman:
The Fienberg Problem: How to Allow Human Interactive Data Analysis in the Age of Differential Privacy. J. Priv. Confidentiality 8(1) (2018) - [j8]Mark Bun, Jonathan R. Ullman, Salil P. Vadhan:
Fingerprinting Codes and the Price of Approximate Differential Privacy. SIAM J. Comput. 47(5): 1888-1938 (2018) - [c36]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs:
Hardness of Non-interactive Differential Privacy from One-Way Functions. CRYPTO (1) 2018: 437-466 - [c35]Albert Cheu, Ravi Sundaram, Jonathan R. Ullman:
Skyline Identification in Multi-Arm Bandits. ISIT 2018: 1006-1010 - [c34]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. NeurIPS 2018: 2381-2390 - [c33]Jonathan R. Ullman, Adam D. Smith, Kobbi Nissim, Uri Stemmer, Thomas Steinke:
The Limits of Post-Selection Generalization. NeurIPS 2018: 6402-6411 - [i39]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. CoRR abs/1802.07128 (2018) - [i38]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. CoRR abs/1805.00216 (2018) - [i37]Kobbi Nissim, Adam D. Smith, Thomas Steinke, Uri Stemmer, Jonathan R. Ullman:
The Limits of Post-Selection Generalization. CoRR abs/1806.06100 (2018) - [i36]Albert Cheu, Adam D. Smith, Jonathan R. Ullman, David Zeber, Maxim Zhilyaev:
Distributed Differential Privacy via Mixnets. CoRR abs/1808.01394 (2018) - [i35]Clément L. Canonne, Gautam Kamath, Audra McMillan, Adam D. Smith, Jonathan R. Ullman:
The Structure of Optimal Private Tests for Simple Hypotheses. CoRR abs/1811.11148 (2018) - [i34]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018) - 2017
- [j7]Jonathan R. Ullman:
Technical Perspective: Building a safety net for data reuse. Commun. ACM 60(4): 85 (2017) - [j6]Mallesh M. Pai, Aaron Roth
An Antifolk Theorem for Large Repeated Games. ACM Trans. Economics and Comput. 5(2): 10:1-10:20 (2017) - [c32]Piotr Indyk, Sepideh Mahabadi, Ronitt Rubinfeld, Jonathan R. Ullman, Ali Vakilian
Fractional Set Cover in the Streaming Model. APPROX-RANDOM 2017: 12:1-12:20 - [c31]Mitali Bafna, Jonathan R. Ullman:
The Price of Selection in Differential Privacy. COLT 2017: 151-168 - [c30]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. FOCS 2017: 552-563 - [c29]Aaron Roth
Multidimensional Dynamic Pricing for Welfare Maximization. EC 2017: 519-536 - [c28]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. SODA 2017: 1306-1325 - [i33]Thomas Steinke, Jonathan R. Ullman:
Subgaussian Tail Bounds via Stability Arguments. CoRR abs/1701.03493 (2017) - [i32]Mitali Bafna, Jonathan R. Ullman:
The Price of Selection in Differential Privacy. CoRR abs/1702.02970 (2017) - [i31]Thomas Steinke, Jonathan R. Ullman:
Tight Lower Bounds for Differentially Private Selection. CoRR abs/1704.03024 (2017) - [i30]Albert Cheu, Ravi Sundaram, Jonathan R. Ullman:
Skyline Identification in Multi-Armed Bandits. CoRR abs/1711.04213 (2017) - [i29]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Daniel Wichs:
Hardness of Non-Interactive Differential Privacy from One-Way Functions. IACR Cryptol. ePrint Arch. 2017: 1107 (2017) - 2016
- [j5]Thomas Steinke, Jonathan R. Ullman:
Between Pure and Approximate Differential Privacy. J. Priv. Confidentiality 7(2) (2016) - [j4]Pavel Hubácek
When Can Limited Randomness Be Used in Repeated Games? Theory Comput. Syst. 59(4): 722-746 (2016) - [j3]Jonathan R. Ullman:
Answering n2+o(1) Counting Queries with Differential Privacy is Hard. SIAM J. Comput. 45(2): 473-496 (2016) - [c27]Foto N. Afrati, Shantanu Sharma, Jeffrey D. Ullman, Jonathan R. Ullman:
Computing Marginals Using MapReduce: Keynote talk paper. IDEAS 2016: 12-23 - [c26]Thomas Steinke, Jonathan R. Ullman:
Interactive fingerprinting codes and the hardness of preventing false discovery. ITA 2016: 1-41 - [c25]Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman:
Privacy Odometers and Filters: Pay-as-you-Go Composition. NIPS 2016: 1921-1929 - [c24]Edo Liberty, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman:
Space Lower Bounds for Itemset Frequency Sketches. PODS 2016: 441-454 - [c23]Jeffrey D. Ullman, Jonathan R. Ullman:
Some pairs problems. BeyondMR@SIGMOD 2016: 8 - [c22]Aaron Roth
Watch and learn: optimizing from revealed preferences feedback. STOC 2016: 949-962 - [c21]Raef Bassily, Kobbi Nissim
Algorithmic stability for adaptive data analysis. STOC 2016: 1046-1059 - [c20]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. TCC (B1) 2016: 659-689 - [r1]Jonathan R. Ullman:
Query Release via Online Learning. Encyclopedia of Algorithms 2016: 1716-1719 - [i28]Jeffrey D. Ullman, Jonathan R. Ullman:
Some Pairs Problems. CoRR abs/1602.01443 (2016) - [i27]Mark Bun, Thomas Steinke, Jonathan R. Ullman:
Make Up Your Mind: The Price of Online Queries in Differential Privacy. CoRR abs/1604.04618 (2016) - [i26]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Salil P. Vadhan:
Privacy Odometers and Filters: Pay-as-you-Go Composition. CoRR abs/1605.08294 (2016) - [i25]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR abs/1607.05397 (2016) - [i24]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. CoRR abs/1607.06141 (2016) - [i23]Marco Gaboardi, James Honaker, Gary King, Kobbi Nissim, Jonathan R. Ullman, Salil P. Vadhan:
PSI (Ψ): a Private data Sharing Interface. CoRR abs/1609.04340 (2016) - [i22]Lucas Kowalczyk, Tal Malkin, Jonathan R. Ullman, Mark Zhandry:
Strong Hardness of Privacy from Weak Traitor Tracing. IACR Cryptol. ePrint Arch. 2016: 721 (2016) - 2015
- [j2]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. 14(1): 101-104 (2015) - [c19]Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. COLT 2015: 1588-1628 - [c18]Cynthia Dwork, Adam D. Smith, Thomas Steinke, Jonathan R. Ullman, Salil P. Vadhan:
Robust Traceability from Trace Amounts. FOCS 2015: 650-669 - [c17]Jonathan R. Ullman:
Private Multiplicative Weights Beyond Linear Queries. PODS 2015: 303-312 - [c16]Pavel Hubácek
When Can Limited Randomness Be Used in Repeated Games? SAGT 2015: 259-271 - [c15]Ryan M. Rogers, Aaron Roth
Inducing Approximately Optimal Flow Using Truthful Mediators. EC 2015: 471-488 - [i21]Thomas Steinke, Jonathan R. Ullman:
Between Pure and Approximate Differential Privacy. CoRR abs/1501.06095 (2015) - [i20]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. CoRR abs/1502.04019 (2015) - [i19]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and Learn: Optimizing from Revealed Preferences Feedback. CoRR abs/1504.01033 (2015) - [i18]Pavel Hubácek, Moni Naor, Jonathan R. Ullman:
When Can Limited Randomness Be Used in Repeated Games? CoRR abs/1507.01191 (2015) - [i17]Foto N. Afrati, Shantanu Sharma, Jeffrey D. Ullman, Jonathan R. Ullman:
Computing Marginals Using MapReduce. CoRR abs/1509.08855 (2015) - [i16]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) - [i15]Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman:
Robust Mediators in Large Games. CoRR abs/1512.02698 (2015) - 2014
- [c14]Moritz Hardt, Jonathan R. Ullman:
Preventing False Discovery in Interactive Data Analysis Is Hard. FOCS 2014: 454-463 - [c13]Justin Hsu
Privately Solving Linear Programs. ICALP (1) 2014: 612-624 - [c12]Karthekeyan Chandrasekaran, Justin Thaler, Jonathan R. Ullman, Andrew Wan:
Faster private release of marginals on small databases. ITCS 2014: 387-402 - [c11]Michael J. Kearns, Mallesh M. Pai, Aaron Roth
Mechanism design in large games: incentives and privacy. ITCS 2014: 403-410 - [c10]Mark Bun, Jonathan R. Ullman, Salil P. Vadhan:
Fingerprinting codes and the price of approximate differential privacy. STOC 2014: 1-10 - [i14]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring. CoRR abs/1402.2801 (2014) - [i13]Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman:
Privately Solving Linear Programs. CoRR abs/1402.3631 (2014) - [i12]Jonathan R. Ullman:
Private Multiplicative Weights Beyond Linear Queries. CoRR abs/1407.1571 (2014) - [i11]Moritz Hardt, Jonathan R. Ullman:
Preventing False Discovery in Interactive Data Analysis is Hard. CoRR abs/1408.1655 (2014) - [i10]Thomas Steinke, Jonathan R. Ullman:
Interactive Fingerprinting Codes and the Hardness of Preventing False Discovery. CoRR abs/1410.1228 (2014) - 2013
- [j1]Anupam Gupta, Moritz Hardt, Aaron Roth
Privately Releasing Conjunctions and the Statistical Query Barrier. SIAM J. Comput. 42(4): 1494-1520 (2013) - [c9]Justin Hsu
Differential privacy for the analyst via private equilibrium computation. STOC 2013: 341-350 - [c8]Jonathan R. Ullman:
Answering n{2+o(1)} counting queries with differential privacy is hard. STOC 2013: 361-370 - [i9]Karthekeyan Chandrasekaran, Justin Thaler, Jonathan R. Ullman, Andrew Wan:
Faster Private Release of Marginals on Small Databases. CoRR abs/1304.3754 (2013) - [i8]Mark Bun, Jonathan R. Ullman, Salil P. Vadhan:
Fingerprinting Codes and the Price of Approximate Differential Privacy. CoRR abs/1311.3158 (2013) - 2012
- [c7]Justin Thaler, Jonathan R. Ullman, Salil P. Vadhan:
Faster Algorithms for Privately Releasing Marginals. ICALP (1) 2012: 810-821 - [c6]Anupam Gupta, Aaron Roth
Iterative Constructions and Private Data Release. TCC 2012: 339-356 - [i7]Justin Thaler, Jonathan R. Ullman, Salil P. Vadhan:
Faster Algorithms for Privately Releasing Marginals. CoRR abs/1205.1758 (2012) - [i6]Jonathan R. Ullman:
Answering n^{2+o(1)} Counting Queries with Differential Privacy is Hard. CoRR abs/1207.6945 (2012) - [i5]Justin Hsu, Aaron Roth, Jonathan R. Ullman:
Differential Privacy for the Analyst via Private Equilibrium Computation. CoRR abs/1211.0877 (2012) - 2011
- [c5]Ian A. Kash, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman:
On the zero-error capacity threshold for deletion channels. ITA 2011: 285-289 - [c4]Anupam Gupta, Moritz Hardt, Aaron Roth
Privately releasing conjunctions and the statistical query barrier. STOC 2011: 803-812 - [c3]Jonathan R. Ullman, Salil P. Vadhan:
PCPs and the Hardness of Generating Private Synthetic Data. TCC 2011: 400-416 - [i4]Ian A. Kash, Michael Mitzenmacher, Justin Thaler, Jonathan R. Ullman:
On the Zero-Error Capacity Threshold for Deletion Channels. CoRR abs/1102.0040 (2011) - [i3]Anupam Gupta, Aaron Roth, Jonathan R. Ullman:
Iterative Constructions and Private Data Release. CoRR abs/1107.3731 (2011) - 2010
- [c2]Shiva Prasad Kasiviswanathan, Mark Rudelson, Adam D. Smith, Jonathan R. Ullman:
The price of privately releasing contingency tables and the spectra of random matrices with correlated rows. STOC 2010: 775-784 - [c1]Scott Duke Kominers
Course Allocation by Proxy Auction. WINE 2010: 551-558 - [i2]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately Releasing Conjunctions and the Statistical Query Barrier. CoRR abs/1011.1296 (2010) - [i1]Jonathan R. Ullman, Salil P. Vadhan:
PCPs and the Hardness of Generating Synthetic Data. Electron. Colloquium Comput. Complex. TR10 (2010)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-25 22:48 CET by the dblp team
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