Alex Kulesza

Publication Activity (10 Years)

Years Active: 2004-2023
Publications (10 Years): 24

Top Topics

Social Networks

Reinforcement Learning

Top Venues

Publications

Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. CoRR (2023)
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
Mean estimation in the add-remove model of differential privacy. CoRR (2023)
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
Subset-Based Instance Optimality in Private Estimation. ICML (2023)
Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
Plume: Differential Privacy at Scale. CoRR (2022)
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Differentially Private Quantiles. CoRR (2021)
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
Differentially Private Quantiles. ICML (2021)
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
Combining Public and Private Data. CoRR (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. NeurIPS (2021)
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
Learning with User-Level Privacy. CoRR (2021)
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Differentially Private Covariance Estimation. NeurIPS (2019)
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes. ICML (2019)
Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy. ICML (2019)
Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
Maximizing Induced Cardinality Under a Determinantal Point Process. NeurIPS (2018)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Completing State Representations using Spectral Learning. NeurIPS (2018)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI (2016)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Improving Predictive State Representations via Gradient Descent. AAAI (2016)
Alex Kulesza, Nan Jiang, Satinder P. Singh
Low-Rank Spectral Learning with Weighted Loss Functions. AISTATS (2015)
Alex Kulesza, Nan Jiang, Satinder P. Singh
Spectral Learning of Predictive State Representations with Insufficient Statistics. AAAI (2015)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Abstraction Selection in Model-based Reinforcement Learning. ICML (2015)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Information Extraction from Larger Multi-layer Social Networks. CoRR (2015)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS (2015)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Socio-Spatial Pareto Frontiers of Twitter Networks. SBP (2015)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Information extraction from large multi-layer social networks. ICASSP (2015)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Socio-Spatial Pareto Frontiers of Twitter Networks. CoRR (2015)
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
Social Collaborative Retrieval. CoRR (2014)
Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar
Expectation-Maximization for Learning Determinantal Point Processes. NIPS (2014)
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
Social collaborative retrieval. WSDM (2014)
Alex Kulesza, N. Raj Rao, Satinder P. Singh
Low-Rank Spectral Learning. AISTATS (2014)
Kai Hong, John M. Conroy, Benoît Favre, Alex Kulesza, Hui Lin, Ani Nenkova
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization. LREC (2014)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Multi-objective Optimization for Multi-level Networks. SBP (2014)
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
Social Collaborative Retrieval. IEEE J. Sel. Top. Signal Process. 8 (4) (2014)
Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar
Expectation-Maximization for Learning Determinantal Point Processes. CoRR (2014)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Multi-Layer Graph Analysis for Dynamic Social Networks. IEEE J. Sel. Top. Signal Process. 8 (4) (2014)
Nematollah Kayhan Batmanghelich, Gerald T. Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn
Diversifying Sparsity Using Variational Determinantal Point Processes. CoRR (2014)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Multi-layer graph analytics for dynamic social networks. CoRR (2013)
Koby Crammer, Alex Kulesza, Mark Dredze
Adaptive regularization of weight vectors. Mach. Learn. 91 (2) (2013)
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
Multi-layer graph analytics for social networks. CAMSAP (2013)
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox, Ben Taskar
Nystrom Approximation for Large-Scale Determinantal Processes. AISTATS (2013)
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox
Markov Determinantal Point Processes. UAI (2012)
Jennifer Gillenwater, Alex Kulesza, Ben Taskar
Near-Optimal MAP Inference for Determinantal Point Processes. NIPS (2012)
Alex Kulesza, Ben Taskar
Learning Determinantal Point Processes CoRR (2012)
Alex Kulesza, Ben Taskar
Determinantal Point Processes for Machine Learning. Found. Trends Mach. Learn. 5 (2-3) (2012)
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox
Markov Determinantal Point Processes CoRR (2012)
Alex Kulesza, Ben Taskar
Determinantal point processes for machine learning CoRR (2012)
Jennifer Gillenwater, Alex Kulesza, Ben Taskar
Discovering Diverse and Salient Threads in Document Collections. EMNLP-CoNLL (2012)
Koby Crammer, Alex Kulesza, Mark Dredze
bounds for the recursive least squares algorithm exploiting input structure. ICASSP (2012)
Alex Kulesza, Ben Taskar
k-DPPs: Fixed-Size Determinantal Point Processes. ICML (2011)
Alex Kulesza, Ben Taskar
Learning Determinantal Point Processes. UAI (2011)
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
A theory of learning from different domains. Mach. Learn. 79 (1-2) (2010)
Alex Kulesza, Ben Taskar
Structured Determinantal Point Processes. NIPS (2010)
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira
Exploiting Feature Covariance in High-Dimensional Online Learning. AISTATS (2010)
Mark Dredze, Alex Kulesza, Koby Crammer
Multi-domain learning by confidence-weighted parameter combination. Mach. Learn. 79 (1-2) (2010)
Koby Crammer, Alex Kulesza, Mark Dredze
Adaptive Regularization of Weight Vectors. NIPS (2009)
Koby Crammer, Mark Dredze, Alex Kulesza
Multi-Class Confidence Weighted Algorithms. EMNLP (2009)
Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns
Empirical Price Modeling for Sponsored Search. WINE (2007)
Alex Kulesza, Fernando Pereira
Structured Learning with Approximate Inference. NIPS (2007)
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
Learning Bounds for Domain Adaptation. NIPS (2007)
John Blatz, Erin Fitzgerald, George F. Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchís, Nicola Ueffing
Confidence Estimation for Machine Translation. COLING (2004)