New Analysis and Algorithm for Learning with Drifting Distributions. - Mehryar Mohri; Andres Muñoz Medina (ALT, 2012)

Learning Theory and Algorithms for revenue optimization in second price auctions with reserve. - Mehryar Mohri; Andres Muñoz Medina (ICML, 2014)

Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers. - Mehryar Mohri; Andres Muñoz Medina (NIPS, 2014)

Revenue Optimization in Posted-Price Auctions with Strategic Buyers. - Mehryar Mohri; Andres Muñoz Medina (CoRR, 2014)

Adaptation Algorithm and Theory Based on Generalized Discrepancy. - Corinna Cortes; Mehryar Mohri; Andres Muñoz Medina (KDD, 2015)

Revenue Optimization against Strategic Buyers. - Mehryar Mohri; Andres Muñoz Medina (NIPS, 2015)

Non-parametric Revenue Optimization for Generalized Second Price auctions.. - Mehryar Mohri; Andres Muñoz Medina (UAI, 2015)

No-Regret Algorithms for Heavy-Tailed Linear Bandits. - Andres Muñoz Medina; Scott Yang (ICML, 2016)

Learning Algorithms for Second-Price Auctions with Reserve. - Mehryar Mohri; Andres Muñoz Medina (J. Mach. Learn. Res., 2016)

Revenue Optimization with Approximate Bid Predictions. - Andres Muñoz Medina; Sergei Vassilvitskii (NIPS, 2017)

Online Learning for Non-Stationary A/B Tests. - Andrés Muñoz Medina; Sergei Vassilvitskii; Dong Yin (CIKM, 2018)

Testing Incentive Compatibility in Display Ad Auctions. - Sébastien Lahaie; Andrés Muñoz Medina; Balasubramanian Sivan; Sergei Vassilvitskii (WWW, 2018)

Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. - Kareem Amin 002; Alex Kulesza; Andres Muñoz Medina; Sergei Vassilvitskii (ICML, 2019)

Differentially Private Covariance Estimation. - Kareem Amin 002; Travis Dick; Alex Kulesza; Andres Muñoz Medina; Sergei Vassilvitskii (NeurIPS, 2019)

Adaptation Based on Generalized Discrepancy. - Corinna Cortes; Mehryar Mohri; Andrés Muñoz Medina (J. Mach. Learn. Res., 2019)

Duff: A Dataset-Distance-Based Utility Function Family for the Exponential Mechanism. - Andrés Muñoz Medina; Jenny Gillenwater (CoRR, 2020)

Private optimization without constraint violations. - Andrés Muñoz Medina; Umar Syed; Sergei Vassilvitskii; Ellen Vitercik (AISTATS, 2021)

Clustering for Private Interest-based Advertising. - Alessandro Epasto; Andrés Muñoz Medina; Steven Avery; Yijian Bai; Róbert Busa-Fekete; CJ Carey; Ya Gao; David Guthrie; Subham Ghosh; James Ioannidis; Junyi Jiao; Jakub Lacki; et al. (KDD, 2021)

A Joint Exponential Mechanism For Differentially Private Top-k. - Jennifer Gillenwater; Matthew Joseph; Andres Muñoz Medina; Mónica Ribero Diaz (ICML, 2022)

Scalable Differentially Private Clustering via Hierarchically Separated Trees. - Vincent Cohen-Addad; Alessandro Epasto; Silvio Lattanzi; Vahab Mirrokni; Andres Muñoz Medina; David Saulpic; Chris Schwiegelshohn; Sergei Vassilvitskii (KDD, 2022)

Private and Communication-Efficient Algorithms for Entropy Estimation. - Gecia Bravo Hermsdorff; Róbert Busa-Fekete; Mohammad Ghavamzadeh; Andrés Muñoz Medina; Umar Syed (NeurIPS, 2022)

Statistical anonymity: Quantifying reidentification risks without reidentifying users. - Gecia Bravo Hermsdorff; Róbert Busa-Fekete; Lee M. Gunderson; Andrés Muñoz Medina; Umar Syed (CoRR, 2022)

Smooth Anonymity for Sparse Binary Matrices. - Hossein Esfandiari; Alessandro Epasto; Vahab S. Mirrokni; Andres Muñoz Medina; Sergei Vassilvitskii (CoRR, 2022)

Label differential privacy and private training data release. - Róbert Istvan Busa-Fekete; Andrés Muñoz Medina; Umar Syed; Sergei Vassilvitskii (ICML, 2023)

Differentially Private Continual Releases of Streaming Frequency Moment Estimations. - Alessandro Epasto; Jieming Mao; Andres Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (ITCS, 2023)

Easy Learning from Label Proportions. - Róbert Busa-Fekete; Heejin Choi; Travis Dick; Claudio Gentile; Andrés Muñoz Medina (NeurIPS, 2023)

A Unified Fast Gradient Clipping Framework for DP-SGD. - Weiwei Kong; Andrés Muñoz Medina (NeurIPS, 2023)

RényiTester: A Variational Approach to Testing Differential Privacy. - William Kong; Andrés Muñoz Medina; Mónica Ribero (CoRR, 2023)

DP-SGD for non-decomposable objective functions. - William Kong; Andrés Muñoz Medina; Mónica Ribero (CoRR, 2023)

Measuring Re-identification Risk. - CJ Carey; Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Shankar Kumar; Andres Muñoz Medina; Vahab Mirrokni; Gabriel Henrique Nunes; Sergei Vassilvitskii; Peilin Zhong (Proc. ACM Manag. Data, 2023)

Auditing Privacy Mechanisms via Label Inference Attacks. - Róbert Busa-Fekete; Travis Dick; Claudio Gentile; Andrés Muñoz Medina; Adam Smith 006; Marika Swanberg (NeurIPS, 2024)

DP-Auditorium: A Large-Scale Library for Auditing Differential Privacy. - William Kong; Andrés Muñoz Medina; Mónica Ribero; Umar Syed (SP, 2024)

Smooth Anonymity for Sparse Graphs. - Alessandro Epasto; Hossein Esfandiari; Vahab Mirrokni; Andrés Muñoz Medina (WWW (Companion Volume), 2024)

Differentially private optimization for non-decomposable objective functions. - Weiwei Kong; Andrés Muñoz Medina; Mónica Ribero (ICLR, 2025)

Differentially Private Synthetic Data Release for Topics API Outputs. - Travis Dick; Alessandro Epasto; Adel Javanmard; Josh Karlin; Andrés Muñoz Medina; Vahab Mirrokni; Sergei Vassilvitskii; Peilin Zhong (CoRR, 2025)