Alex Kulesza
27 papers · 2004–2026 · 7 venues · across top CS/AI venues
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Achievements
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Century Club Β· Bronze
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Keyword Collector
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Renaissance Researcher
(6)
Top co-authors
Research topics
Machine Learning
(21)
Optimization & Theory
(15)
Mathematics & Optimization
(10)
Core Methods
(8)
Learning Types
(7)
Artificial Intelligence
(6)
Application Areas
(6)
Mathematics
(6)
Optimization
(5)
Bayesian & Probabilistic
(4)
Data Science & Analytics
(3)
Security & Privacy
(3)
Applications
(3)
Core AI
(3)
Differential Privacy
(3)
Keywords
differential privacy
(8)
determinantal point process
(6)
empirical risk minimization
(4)
low-rank approximation
(3)
spectral learning
(3)
determinantal point processes
(3)
mean estimation
(3)
online learning
(2)
quantile estimation
(2)
markov random field
(2)
singular value decomposition
(2)
statistical estimation
(2)
structured prediction
(2)
matrix factorization
(2)
kernel matrix
(2)
probabilistic modeling
(2)
submodular optimization
(2)
approximate inference
(2)
covariance matrix
(2)
probabilistic model
(2)
Papers
Ripple Mechanisms for Discrete and Private Statistics
COLT 2026
Approximate Differential Privacy of the β_2 Mechanism
ICML 2025
General Staircase Mechanisms for Optimal Differential Privacy
AISTATS 2025
Mean Estimation in the Add-Remove Model of Differential Privacy
ICML 2024
Subset-Based Instance Optimality in Private Estimation
ICML 2023
Differentially Private Quantiles
ICML 2021
Learning with User-Level Privacy
NeurIPS 2021
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
ICML 2019
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
ICML 2019
Differentially Private Covariance Estimation
NeurIPS 2019
Completing State Representations using Spectral Learning
NeurIPS 2018
Maximizing Induced Cardinality Under a Determinantal Point Process
NeurIPS 2018
The Dependence of Effective Planning Horizon on Model Accuracy
IJCAI 2016
Abstraction Selection in Model-based Reinforcement Learning
ICML 2015
Low-Rank Spectral Learning with Weighted Loss Functions
AISTATS 2015
Expectation-Maximization for Learning Determinantal Point Processes
NeurIPS 2014
Low-Rank Spectral Learning
AISTATS 2014
NystrΓΆm Approximation for Large-Scale Determinantal Processes
AISTATS 2013
Discovering Diverse and Salient Threads in Document Collections
EMNLP 2012
Near-Optimal MAP Inference for Determinantal Point Processes
NeurIPS 2012
Exploiting Feature Covariance in High-Dimensional Online Learning
AISTATS 2010
Structured Determinantal Point Processes
NeurIPS 2010
Adaptive Regularization of Weight Vectors
NeurIPS 2009
Multi-Class Confidence Weighted Algorithms
EMNLP 2009
Learning Bounds for Domain Adaptation
NeurIPS 2007
Structured Learning with Approximate Inference
NeurIPS 2007
Confidence Estimation for Machine Translation
COLING 2004