Kulesza, Alex

34 publications

ICML 2025 Approximate Differential Privacy of the $\ell_2$ Mechanism Matthew Joseph, Alex Kulesza, Alexander Yu

AISTATS 2025 General Staircase Mechanisms for Optimal Differential Privacy Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang

NeurIPS 2025 Private Set Union with Multiple Contributions Travis Dick, Haim Kaplan, Alex Kulesza, Uri Stemmer, Ziteng Sun, Ananda Theertha Suresh

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

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

NeurIPSW 2021 Combining Public and Private Data Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza

ICML 2021 Differentially Private Quantiles Jennifer Gillenwater, Matthew Joseph, Alex Kulesza

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

ICML 2019 A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii

ICML 2019 Bounding User Contributions: A Bias-Variance Trade-Off in Differential Privacy Kareem Amin, Alex Kulesza, Andres Munoz, Sergei Vassilvtiskii

NeurIPS 2019 Differentially Private Covariance Estimation Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

NeurIPS 2018 Completing State Representations Using Spectral Learning Nan Jiang, Alex Kulesza, Satinder Singh

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

AAAI 2016 Improving Predictive State Representations via Gradient Descent Nan Jiang, Alex Kulesza, Satinder Singh

IJCAI 2016 The Dependence of Effective Planning Horizon on Model Accuracy Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis

ICML 2015 Abstraction Selection in Model-Based Reinforcement Learning Nan Jiang, Alex Kulesza, Satinder Singh

AISTATS 2015 Low-Rank Spectral Learning with Weighted Loss Functions Alex Kulesza, Nan Jiang, Satinder Singh

AAAI 2015 Spectral Learning of Predictive State Representations with Insufficient Statistics Alex Kulesza, Nan Jiang, Satinder Singh

NeurIPS 2014 Expectation-Maximization for Learning Determinantal Point Processes Jennifer A Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar

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

MLJ 2013 Adaptive Regularization of Weight Vectors Koby Crammer, Alex Kulesza, Mark Dredze

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

FnTML 2012 Determinantal Point Processes for Machine Learning Alex Kulesza, Ben Taskar

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

NeurIPS 2012 Near-Optimal MAP Inference for Determinantal Point Processes Jennifer Gillenwater, Alex Kulesza, Ben Taskar

ICML 2011 K-DPPs: Fixed-Size Determinantal Point Processes Alex Kulesza, Ben Taskar

UAI 2011 Learning Determinantal Point Processes Alex Kulesza, Ben Taskar

MLJ 2010 A Theory of Learning from Different Domains Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan

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

MLJ 2010 Multi-Domain Learning by Confidence-Weighted Parameter Combination Mark Dredze, Alex Kulesza, Koby Crammer

NeurIPS 2010 Structured Determinantal Point Processes Alex Kulesza, Ben Taskar

NeurIPS 2009 Adaptive Regularization of Weight Vectors Koby Crammer, Alex Kulesza, Mark Dredze

NeurIPS 2007 Learning Bounds for Domain Adaptation John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman

NeurIPS 2007 Structured Learning with Approximate Inference Alex Kulesza, Fernando Pereira