Graphical Models for Bandit Problems. - Kareem Amin 002; Michael J. Kearns; Umar Syed (UAI, 2011)

Bandits, Query Learning, and the Haystack Dimension. - Kareem Amin 002; Michael J. Kearns; Umar Syed (COLT, 2011)

Budget Optimization for Sponsored Search: Censored Learning in MDPs. - Kareem Amin 002; Michael J. Kearns; Peter B. Key; Anton Schwaighofer (UAI, 2012)

Large-Scale Bandit Problems and KWIK Learning. - Jacob D. Abernethy; Kareem Amin 002; Michael J. Kearns; Moez Draief (ICML (1), 2013)

Learning Prices for Repeated Auctions with Strategic Buyers. - Kareem Amin 002; Afshin Rostamizadeh; Umar Syed (NIPS, 2013)

Learning from Contagion (Without Timestamps). - Kareem Amin 002; Hoda Heidari; Michael J. Kearns (ICML, 2014)

Repeated Contextual Auctions with Strategic Buyers. - Kareem Amin 002; Afshin Rostamizadeh; Umar Syed (NIPS, 2014)

Online Learning and Profit Maximization from Revealed Preferences. - Kareem Amin 002; Rachel Cummings; Lili Dworkin; Michael J. Kearns; Aaron Roth 001 (AAAI, 2015)

Budgeted Prediction with Expert Advice. - Kareem Amin 002; Satyen Kale; Gerald Tesauro; Deepak S. Turaga (AAAI, 2015)

A moving target defense approach to mitigate DDoS attacks against proxy-based architectures. - Sridhar Venkatesan; Massimiliano Albanese; Kareem Amin 002; Sushil Jajodia; Mason Wright (CNS, 2016)

Threshold Bandits, With and Without Censored Feedback. - Jacob D. Abernethy; Kareem Amin 002; Ruihao Zhu (NIPS, 2016)

Strategic Payment Routing in Financial Credit Networks. - Frank Cheng; Junming Liu; Kareem Amin 002; Michael P. Wellman (EC, 2016)

Gradient Methods for Stackelberg Games. - Kareem Amin 002; Michael P. Wellman; Satinder Singh 001 (UAI, 2016)

Towards Resolving Unidentifiability in Inverse Reinforcement Learning. - Kareem Amin 002; Satinder Singh 001 (CoRR, 2016)

Repeated Inverse Reinforcement Learning. - Kareem Amin 002; Nan Jiang 008; Satinder Singh 001 (NIPS, 2017)

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)

Understanding the Effects of Batching in Online Active Learning. - Kareem Amin 002; Corinna Cortes; Giulia DeSalvo; Afshin Rostamizadeh (AISTATS, 2020)

Pan-Private Uniformity Testing. - Kareem Amin 002; Matthew Joseph; Jieming Mao (COLT, 2020)

Learning with Labeling Induced Abstentions. - Kareem Amin 002; Giulia DeSalvo; Afshin Rostamizadeh (NeurIPS, 2021)

Learning with User-Level Privacy. - Daniel Levy; Ziteng Sun; Kareem Amin 002; Satyen Kale; Alex Kulesza; Mehryar Mohri; Ananda Theertha Suresh (NeurIPS, 2021)

Plume: Differential Privacy at Scale. - Kareem Amin 002; Jennifer Gillenwater; Matthew Joseph; Alex Kulesza; Sergei Vassilvitskii (CoRR, 2022)

Private Algorithms with Private Predictions. - Kareem Amin 002; Travis Dick; Mikhail Khodak; Sergei Vassilvitskii (CoRR, 2022)

Easy Differentially Private Linear Regression. - Kareem Amin 002; Matthew Joseph; Mónica Ribero; Sergei Vassilvitskii (ICLR, 2023)

Learning-augmented private algorithms for multiple quantile release. - Mikhail Khodak; Kareem Amin 002; Travis Dick; Sergei Vassilvitskii (ICML, 2023)

Private prediction for large-scale synthetic text generation. - Kareem Amin 002; Alex Bie; Weiwei Kong; Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Andreas Terzis; Sergei Vassilvitskii (EMNLP (Findings), 2024)

Practical Considerations for Differential Privacy. - Kareem Amin 002; Alex Kulesza; Sergei Vassilvitskii (CoRR, 2024)

Escaping Collapse: The Strength of Weak Data for Large Language Model Training. - Kareem Amin 002; Sara Babakniya; Alex Bie; Weiwei Kong; Umar Syed; Sergei Vassilvitskii (CoRR, 2025)

Clustering and Median Aggregation Improve Differentially Private Inference. - Kareem Amin 002; Salman Avestimehr; Sara Babakniya; Alex Bie; Weiwei Kong; Natalia Ponomareva 001; Umar Syed (CoRR, 2025)