Using a mixture of probabilistic decision trees for direct prediction of protein function. - Umar Syed; Golan Yona (RECOMB, 2003)

A Game-Theoretic Approach to Apprenticeship Learning. - Umar Syed; Robert E. Schapire (NIPS, 2007)

Imitation Learning with a Value-Based Prior. - Umar Syed; Robert E. Schapire (UAI, 2007)

Using Automatically Transcribed Dialogs to Learn User Models in a Spoken Dialog System. - Umar Syed; Jason D. Williams (ACL (2), 2008)

Apprenticeship learning using linear programming. - Umar Syed; Michael H. Bowling; Robert E. Schapire (ICML, 2008)

Adapting to the Shifting Intent of Search Queries. - Umar Syed; Aleksandrs Slivkins; Nina Mishra (NIPS, 2009)

Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts. - Mickey Brautbar; Michael J. Kearns; Umar Syed (AAAI, 2010)

A Reduction from Apprenticeship Learning to Classification. - Umar Syed; Robert E. Schapire (NIPS, 2010)

Semi-Supervised Learning with Adversarially Missing Label Information. - Umar Syed; Ben Taskar (NIPS, 2010)

There's something about MRAI: Timing diversity can exponentially worsen BGP convergence. - Alex Fabrikant; Umar Syed; Jennifer Rexford (INFOCOM, 2011)

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)

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

Deep Boosting. - Corinna Cortes; Mehryar Mohri; Umar Syed (ICML, 2014)

Corporate learning at scale: lessons from a large online course at google. - Arthur Asuncion; Jac de Haan; Mehryar Mohri; Kayur Patel; Afshin Rostamizadeh; Umar Syed; Lauren Wong (L@S, 2014)

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

Multi-Class Deep Boosting. - Vitaly Kuznetsov; Mehryar Mohri; Umar Syed (NIPS, 2014)

Learning with Deep Cascades. - Giulia DeSalvo; Mehryar Mohri; Umar Syed (ALT, 2015)

An Optimal Online Algorithm For Retrieving Heavily Perturbed Statistical Databases In The Low-Dimensional Querying Model. - Krzysztof Marcin Choromanski; Afshin Rostamizadeh; Umar Syed (CIKM, 2015)

Structural Maxent Models. - Corinna Cortes; Vitaly Kuznetsov; Mehryar Mohri; Umar Syed (ICML, 2015)

An $ tilde{O}( frac{1}{ sqrt{T}})$-error online algorithm for retrieving heavily perturbated statistical databases in the low-dimensional querying mode. - Krzysztof Choromanski; Afshin Rostamizadeh; Umar Syed (CoRR, 2015)

Pricing a Low-regret Seller. - Hoda Heidari; Mohammad Mahdian; Umar Syed; Sergei Vassilvitskii; Sadra Yazdanbod (ICML, 2016)

Where to Sell: Simulating Auctions From Learning Algorithms. - Hamid Nazerzadeh; Renato Paes Leme; Afshin Rostamizadeh; Umar Syed (EC, 2016)

SQML: large-scale in-database machine learning with pure SQL. - Umar Syed; Sergei Vassilvitskii (SoCC, 2017)

Statistical Cost Sharing. - Eric Balkanski; Umar Syed; Sergei Vassilvitskii (NIPS, 2017)

Inmate OS and VMM based Secured Virtualization for Cloud Encompassment. - Umar Syed (Int. J. Web Appl., 2018)

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)

Label differential privacy via clustering. - Hossein Esfandiari; Vahab S. Mirrokni; Umar Syed; Sergei Vassilvitskii (AISTATS, 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)

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

Harnessing large-language models to generate private synthetic text. - Alexey Kurakin; Natalia Ponomareva 001; Umar Syed; Liam MacDermed; Andreas Terzis (CoRR, 2023)

Lexicographic Optimization: Algorithms and Stability. - Jacob D. Abernethy; Robert E. Schapire; Umar Syed (AISTATS, 2024)

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)

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

Assessing Web Fingerprinting Risk. - Enrico Bacis; Igor Bilogrevic; Róbert Busa-Fekete; Asanka Herath; Antonio Sartori; Umar Syed (WWW (Companion Volume), 2024)

Near-optimal algorithms for private estimation and sequential testing of collision probability. - Róbert Istvan Busa-Fekete; Umar Syed (AISTATS, 2025)

How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users. - Alex Berke; Badih Ghazi; Enrico Bacis; Pritish Kamath; Ravi Kumar 001; Robin Lassonde; Pasin Manurangsi; Umar Syed (Proc. Priv. Enhancing Technol., 2025)

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)