Alex Kulesza

64 papers A* 16A 9B 2Misc 2Journal 24Unranked 11
YearRankTypeTitle / Venue / Authors
2026 J jnl
AI-rithmetic.
CoRR
Alex Bie, Travis Dick, Alex Kulesza, Prabhakar Raghavan, Vinod Raman, Sergei Vassilvitskii
2025 J jnl
Approximate Differential Privacy of the ℓ
CoRR
Matthew Joseph, Alex Kulesza, Alexander Yu
2025 A* conf
Approximate Differential Privacy of the ℓ2 Mechanism.
ICML
Matthew Joseph, Alex Kulesza, Alexander Yu
2025 A conf
General Staircase Mechanisms for Optimal Differential Privacy.
AISTATS
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
2024 A* conf
Mean Estimation in the Add-Remove Model of Differential Privacy.
ICML
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
2024 J jnl
Practical Considerations for Differential Privacy.
CoRR
Kareem Amin, Alex Kulesza, Sergei Vassilvitskii
2023 J jnl
Mean estimation in the add-remove model of differential privacy.
CoRR
Alex Kulesza, Ananda Theertha Suresh, Yuyan Wang
2023 A* conf
Subset-Based Instance Optimality in Private Estimation.
ICML
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
2023 J jnl
Subset-Based Instance Optimality in Private Estimation.
CoRR
Travis Dick, Alex Kulesza, Ziteng Sun, Ananda Theertha Suresh
2022 J jnl
Plume: Differential Privacy at Scale.
CoRR
Kareem Amin, Jennifer Gillenwater, Matthew Joseph, Alex Kulesza, Sergei Vassilvitskii
2021 J jnl
Combining Public and Private Data.
CoRR
Cecilia Ferrando, Jennifer Gillenwater, Alex Kulesza
2021 A* conf
Differentially Private Quantiles.
ICML
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
2021 J jnl
Differentially Private Quantiles.
CoRR
Jennifer Gillenwater, Matthew Joseph, Alex Kulesza
2021 A* conf
Learning with User-Level Privacy.
NeurIPS
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
2021 J jnl
Learning with User-Level Privacy.
CoRR
Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, Ananda Theertha Suresh
2019 A* conf
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes.
ICML
Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvitskii
2019 A* conf
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy.
ICML
Kareem Amin, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
2019 A* conf
Differentially Private Covariance Estimation.
NeurIPS
Kareem Amin, Travis Dick, Alex Kulesza, Andres Muñoz Medina, Sergei Vassilvitskii
2018 A* conf
Completing State Representations using Spectral Learning.
NeurIPS
Nan Jiang, Alex Kulesza, Satinder Singh
2018 A* conf
Maximizing Induced Cardinality Under a Determinantal Point Process.
NeurIPS
Jennifer A. Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet
2016 A* conf
Improving Predictive State Representations via Gradient Descent.
AAAI
Nan Jiang, Alex Kulesza, Satinder Singh
2016 A* conf
The Dependence of Effective Planning Horizon on Model Accuracy.
IJCAI
Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2015 A* conf
Abstraction Selection in Model-based Reinforcement Learning.
ICML
Nan Jiang, Alex Kulesza, Satinder Singh
2015 J jnl
Information Extraction from Larger Multi-layer Social Networks.
CoRR
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2015 Misc conf
Information extraction from large multi-layer social networks.
ICASSP
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2015 A conf
Low-Rank Spectral Learning with Weighted Loss Functions.
AISTATS
Alex Kulesza, Nan Jiang, Satinder Singh
2015 — conf
Socio-Spatial Pareto Frontiers of Twitter Networks.
SBP
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2015 J jnl
Socio-Spatial Pareto Frontiers of Twitter Networks.
CoRR
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2015 A* conf
Spectral Learning of Predictive State Representations with Insufficient Statistics.
AAAI
Alex Kulesza, Nan Jiang, Satinder Singh
2015 A conf
The Dependence of Effective Planning Horizon on Model Accuracy.
AAMAS
Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2014 B conf
A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization.
LREC
Kai Hong, John M. Conroy, Benoît Favre, Alex Kulesza, Hui Lin, Ani Nenkova
2014 J jnl
Diversifying Sparsity Using Variational Determinantal Point Processes.
CoRR
Nematollah Kayhan Batmanghelich, Gerald T. Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn
2014 — conf
Expectation-Maximization for Learning Determinantal Point Processes.
NIPS
Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Benjamin Taskar
2014 J jnl
Expectation-Maximization for Learning Determinantal Point Processes.
CoRR
Jennifer Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar
2014 A conf
Low-Rank Spectral Learning.
AISTATS
Alex Kulesza, N. Raj Rao, Satinder Singh
2014 J jnl
Multi-Layer Graph Analysis for Dynamic Social Networks.
IEEE J. Sel. Top. Signal Process.
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2014 — conf
Multi-objective Optimization for Multi-level Networks.
SBP
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2014 J jnl
Social Collaborative Retrieval.
CoRR
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
2014 J jnl
Social Collaborative Retrieval.
IEEE J. Sel. Top. Signal Process.
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
2014 A conf
Social collaborative retrieval.
WSDM
Ko-Jen Hsiao, Alex Kulesza, Alfred O. Hero III
2013 J jnl
Adaptive regularization of weight vectors.
Mach. Learn.
Koby Crammer, Alex Kulesza, Mark Dredze
2013 J jnl
Multi-layer graph analytics for dynamic social networks.
CoRR
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2013 — conf
Multi-layer graph analytics for social networks.
CAMSAP
Brandon Oselio, Alex Kulesza, Alfred O. Hero III
2013 A conf
Nystrom Approximation for Large-Scale Determinantal Processes.
AISTATS
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox, Ben Taskar
2012 J jnl
Determinantal Point Processes for Machine Learning.
Found. Trends Mach. Learn.
Alex Kulesza, Ben Taskar
2012 J jnl
Determinantal point processes for machine learning
CoRR
Alex Kulesza, Ben Taskar
2012 — conf
Discovering Diverse and Salient Threads in Document Collections.
EMNLP-CoNLL
Jennifer Gillenwater, Alex Kulesza, Ben Taskar
2012 J jnl
Learning Determinantal Point Processes
CoRR
Alex Kulesza, Ben Taskar
2012 J jnl
Markov Determinantal Point Processes
CoRR
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox
2012 A conf
Markov Determinantal Point Processes.
UAI
Raja Hafiz Affandi, Alex Kulesza, Emily B. Fox
2012 — conf
Near-Optimal MAP Inference for Determinantal Point Processes.
NIPS
Jennifer Gillenwater, Alex Kulesza, Ben Taskar
2012 Misc conf
New ℌ
ICASSP
Koby Crammer, Alex Kulesza, Mark Dredze
2011 A conf
Learning Determinantal Point Processes.
UAI
Alex Kulesza, Ben Taskar
2011 A* conf
k-DPPs: Fixed-Size Determinantal Point Processes.
ICML
Alex Kulesza, Ben Taskar
2010 J jnl
A theory of learning from different domains.
Mach. Learn.
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan
2010 A conf
Exploiting Feature Covariance in High-Dimensional Online Learning.
AISTATS
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira
2010 J jnl
Multi-domain learning by confidence-weighted parameter combination.
Mach. Learn.
Mark Dredze, Alex Kulesza, Koby Crammer
2010 — conf
Structured Determinantal Point Processes.
NIPS
Alex Kulesza, Ben Taskar
2009 — conf
Adaptive Regularization of Weight Vectors.
NIPS
Koby Crammer, Alex Kulesza, Mark Dredze
2009 A* conf
Multi-Class Confidence Weighted Algorithms.
EMNLP
Koby Crammer, Mark Dredze, Alex Kulesza
2007 — conf
Empirical Price Modeling for Sponsored Search.
WINE
Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, Michael J. Kearns
2007 — conf
Learning Bounds for Domain Adaptation.
NIPS
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
2007 — conf
Structured Learning with Approximate Inference.
NIPS
Alex Kulesza, Fernando Pereira
2004 B conf
Confidence Estimation for Machine Translation.
COLING
John Blatz, Erin Fitzgerald, George F. Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchís, Nicola Ueffing