Confidence Estimation for Machine Translation. - John Blatz; Erin Fitzgerald; George F. Foster; Simona Gandrabur; Cyril Goutte; Alex Kulesza; Alberto Sanchís; Nicola Ueffing (COLING, 2004)

Learning Bounds for Domain Adaptation. - John Blitzer; Koby Crammer; Alex Kulesza; Fernando Pereira 003; Jennifer Wortman (NIPS, 2007)

Structured Learning with Approximate Inference. - Alex Kulesza; Fernando Pereira (NIPS, 2007)

Empirical Price Modeling for Sponsored Search. - Kuzman Ganchev; Alex Kulesza; Jinsong Tan; Ryan Gabbard; Qian Liu; Michael J. Kearns (WINE, 2007)

Multi-Class Confidence Weighted Algorithms. - Koby Crammer; Mark Dredze; Alex Kulesza (EMNLP, 2009)

Structured Determinantal Point Processes. - Alex Kulesza; Ben Taskar (NIPS, 2010)

Exploiting Feature Covariance in High-Dimensional Online Learning. - Justin Ma; Alex Kulesza; Mark Dredze; Koby Crammer; Lawrence K. Saul; Fernando Pereira 003 (AISTATS, 2010)

A theory of learning from different domains. - Shai Ben-David; John Blitzer; Koby Crammer; Alex Kulesza; Fernando Pereira 003; Jennifer Wortman Vaughan (Mach. Learn., 2010)

Multi-domain learning by confidence-weighted parameter combination. - Mark Dredze; Alex Kulesza; Koby Crammer (Mach. Learn., 2010)

k-DPPs: Fixed-Size Determinantal Point Processes. - Alex Kulesza; Ben Taskar (ICML, 2011)

Learning Determinantal Point Processes. - Alex Kulesza; Ben Taskar (UAI, 2011)

Discovering Diverse and Salient Threads in Document Collections. - Jennifer Gillenwater; Alex Kulesza; Ben Taskar (EMNLP-CoNLL, 2012)

New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure. - Koby Crammer; Alex Kulesza; Mark Dredze (ICASSP, 2012)

Near-Optimal MAP Inference for Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Ben Taskar (NIPS, 2012)

Markov Determinantal Point Processes. - Raja Hafiz Affandi; Alex Kulesza; Emily B. Fox (UAI, 2012)

Determinantal Point Processes for Machine Learning. - Alex Kulesza; Ben Taskar (Found. Trends Mach. Learn., 2012)

Nystrom Approximation for Large-Scale Determinantal Processes. - Raja Hafiz Affandi; Alex Kulesza; Emily B. Fox; Ben Taskar (AISTATS, 2013)

Multi-layer graph analytics for social networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (CAMSAP, 2013)

Multi-layer graph analytics for dynamic social networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (CoRR, 2013)

Adaptive regularization of weight vectors. - Koby Crammer; Alex Kulesza; Mark Dredze (Mach. Learn., 2013)

Low-Rank Spectral Learning. - Alex Kulesza; N. Raj Rao; Satinder Singh 001 (AISTATS, 2014)

A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization. - Kai Hong; John M. Conroy; Benoît Favre; Alex Kulesza; Hui Lin; Ani Nenkova (LREC, 2014)

Expectation-Maximization for Learning Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Emily B. Fox; Benjamin Taskar (NIPS, 2014)

Multi-objective Optimization for Multi-level Networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (SBP, 2014)

Diversifying Sparsity Using Variational Determinantal Point Processes. - Nematollah Kayhan Batmanghelich; Gerald T. Quon; Alex Kulesza; Manolis Kellis; Polina Golland; Luke Bornn (CoRR, 2014)

Social Collaborative Retrieval. - Ko-Jen Hsiao; Alex Kulesza; Alfred O. Hero III (IEEE J. Sel. Top. Signal Process., 2014)

Multi-Layer Graph Analysis for Dynamic Social Networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (IEEE J. Sel. Top. Signal Process., 2014)

Spectral Learning of Predictive State Representations with Insufficient Statistics. - Alex Kulesza; Nan Jiang 008; Satinder Singh 001 (AAAI, 2015)

Low-Rank Spectral Learning with Weighted Loss Functions. - Alex Kulesza; Nan Jiang 008; Satinder Singh 001 (AISTATS, 2015)

Information extraction from large multi-layer social networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (ICASSP, 2015)

Abstraction Selection in Model-based Reinforcement Learning. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (ICML, 2015)

Socio-Spatial Pareto Frontiers of Twitter Networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (SBP, 2015)

Information Extraction from Larger Multi-layer Social Networks. - Brandon Oselio; Alex Kulesza; Alfred O. Hero III (CoRR, 2015)

Improving Predictive State Representations via Gradient Descent. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (AAAI, 2016)

The Dependence of Effective Planning Horizon on Model Accuracy. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001; Richard L. Lewis (IJCAI, 2016)

Maximizing Induced Cardinality Under a Determinantal Point Process. - Jennifer A. Gillenwater; Alex Kulesza; Sergei Vassilvitskii; Zelda E. Mariet (NeurIPS, 2018)

Completing State Representations using Spectral Learning. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (NeurIPS, 2018)

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

A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. - Jennifer Gillenwater; Alex Kulesza; Zelda Mariet; Sergei Vassilvitskii (ICML, 2019)

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

Differentially Private Quantiles. - Jennifer Gillenwater; Matthew Joseph; Alex Kulesza (ICML, 2021)

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

Combining Public and Private Data. - Cecilia Ferrando; Jennifer Gillenwater; Alex Kulesza (CoRR, 2021)

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

Subset-Based Instance Optimality in Private Estimation. - Travis Dick; Alex Kulesza; Ziteng Sun; Ananda Theertha Suresh (ICML, 2023)

Mean Estimation in the Add-Remove Model of Differential Privacy. - Alex Kulesza; Ananda Theertha Suresh; Yuyan Wang (ICML, 2024)

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

General Staircase Mechanisms for Optimal Differential Privacy. - Alex Kulesza; Ananda Theertha Suresh; Yuyan Wang (AISTATS, 2025)

Approximate Differential Privacy of the ℓ2 Mechanism. - Matthew Joseph; Alex Kulesza; Alexander Yu (CoRR, 2025)