| 2026 | STOC | Testing Distributions against Bounded Distinguishers. | Mark Bun, Rathin Desai, Renato Ferreira Pinto Jr. |
| 2024 | ALT | Not All Learnable Distribution Classes are Privately Learnable. | Mark Bun, Gautam Kamath, Argyris Mouzakis, Vikrant Singhal |
| 2024 | ALT | Private PAC Learning May be Harder than Online Learning. | Mark Bun, Aloni Cohen, Rathin Desai |
| 2023 | STOC | Stability Is Stable: Connections between Replicability, Privacy, and Adaptive Generalization. | Mark Bun, Marco Gaboardi, Max Hopkins, Russell Impagliazzo, Rex Lei, Toniann Pitassi, Satchit Sivakumar, Jessica Sorrell |
| 2022 | COLT | Strong Memory Lower Bounds for Learning Natural Models. | Gavin Brown, Mark Bun, Adam D. Smith |
| 2021 | ICML | Differentially Private Correlation Clustering. | Mark Bun, Marek Elis, Janardhan Kulkarni |
| 2021 | STOC | When is memorization of irrelevant training data necessary for high-accuracy learning? | Gavin Brown, Mark Bun, Vitaly Feldman, Adam D. Smith, Kunal Talwar |
| 2020 | COLT | Efficient, Noise-Tolerant, and Private Learning via Boosting. | Mark Bun, Marco Leandro Carmosino, Jessica Sorrell |
| 2020 | FOCS | An Equivalence Between Private Classification and Online Prediction. | Mark Bun, Roi Livni, Shay Moran |
| 2020 | ICML | New Oracle-Efficient Algorithms for Private Synthetic Data Release. | Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, Zhiwei Steven Wu |
| 2019 | ICALP | Sign-Rank Can Increase Under Intersection. | Mark Bun, Nikhil S. Mande, Justin Thaler |
| 2019 | SODA | Towards Instance-Optimal Private Query Release. | Jaroslaw Blasiok, Mark Bun, Aleksandar Nikolov, Thomas Steinke |
| 2019 | SODA | Quantum algorithms and approximating polynomials for composed functions with shared inputs. | Mark Bun, Robin Kothari, Justin Thaler |
| 2018 | PODS | Heavy Hitters and the Structure of Local Privacy. | Mark Bun, Jelani Nelson, Uri Stemmer |
| 2018 | STOC | Composable and versatile privacy via truncated CDP. | Mark Bun, Cynthia Dwork, Guy N. Rothblum, Thomas Steinke |
| 2018 | STOC | The polynomial method strikes back: tight quantum query bounds via dual polynomials. | Mark Bun, Robin Kothari, Justin Thaler |
| 2017 | FOCS | A Nearly Optimal Lower Bound on the Approximate Degree of AC | Mark Bun, Justin Thaler |
| 2017 | ICML | Differentially Private Submodular Maximization: Data Summarization in Disguise. | Marko Mitrovic, Mark Bun, Andreas Krause, Amin Karbasi |
| 2017 | SODA | Make Up Your Mind: The Price of Online Queries in Differential Privacy. | Mark Bun, Thomas Steinke, Jonathan R. Ullman |
| 2016 | ICALP | Improved Bounds on the Sign-Rank of AC^0. | Mark Bun, Justin Thaler |
| 2016 | TCC | Separating Computational and Statistical Differential Privacy in the Client-Server Model. | Mark Bun, Yi-Hsiu Chen, Salil P. Vadhan |
| 2016 | TCC | Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds. | Mark Bun, Thomas Steinke |
| 2016 | TCC | Order-Revealing Encryption and the Hardness of Private Learning. | Mark Bun, Mark Zhandry |
| 2015 | FOCS | Differentially Private Release and Learning of Threshold Functions. | Mark Bun, Kobbi Nissim, Uri Stemmer, Salil P. Vadhan |
| 2015 | ICALP | Hardness Amplification and the Approximate Degree of Constant-Depth Circuits. | Mark Bun, Justin Thaler |
| 2014 | STOC | Fingerprinting codes and the price of approximate differential privacy. | Mark Bun, Jonathan R. Ullman, Salil P. Vadhan |
| 2013 | ICALP | Dual Lower Bounds for Approximate Degree and Markov-Bernstein Inequalities. | Mark Bun, Justin Thaler |