Bun, Mark

15 publications

ALT 2024 Not All Learnable Distribution Classes Are Privately Learnable Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal

NeurIPS 2024 Optimal Hypothesis Selection in (Almost) Linear Time Maryam Aliakbarpour, Mark Bun, Adam Smith

NeurIPS 2024 Oracle-Efficient Differentially Private Learning with Public Data Adam Block, Mark Bun, Rathin Desai, Abhishek Shetty, Zhiwei Steven Wu

ALT 2024 Private PAC Learning May Be Harder than Online Learning Mark Bun, Aloni Cohen, Rathin Desai

NeurIPS 2023 Hypothesis Selection with Memory Constraints Maryam Aliakbarpour, Mark Bun, Adam Smith

COLT 2022 Strong Memory Lower Bounds for Learning Natural Models Gavin Brown, Mark Bun, Adam Smith

ICML 2021 Differentially Private Correlation Clustering Mark Bun, Marek Elias, Janardhan Kulkarni

NeurIPS 2021 Multiclass Versus Binary Differentially Private PAC Learning Satchit Sivakumar, Mark Bun, Marco Gaboardi

NeurIPS 2020 A Computational Separation Between Private Learning and Online Learning Mark Bun

COLT 2020 Efficient, Noise-Tolerant, and Private Learning via Boosting Mark Bun, Marco Leandro Carmosino, Jessica Sorrell

ICML 2020 New Oracle-Efficient Algorithms for Private Synthetic Data Release Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Steven Wu

NeurIPS 2019 Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation Mark Bun, Thomas Steinke

NeurIPS 2019 Private Hypothesis Selection Mark Bun, Gautam Kamath, Thomas Steinke, Steven Z. Wu

JMLR 2019 Simultaneous Private Learning of Multiple Concepts Mark Bun, Kobbi Nissim, Uri Stemmer

ICML 2017 Differentially Private Submodular Maximization: Data Summarization in Disguise Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi