Alex Kulesza
27 papers · 2004–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (5) πΊοΈ Taxonomy Completionist (17) π£ Hot Topic Early Bird
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Academic Marathon
(21)
π
Keyword Trendsetter Combo
(3)
π±
Topic Pioneer
π
Keyword Champion
(2)
π
Trend Setter
π
Century Club
(27)
π
Conference Pioneer
ποΈ
Keyword Collector
(98)
π₯
Unstoppable
(5)
Conferences
NIPS (10)
ICML (7)
AISTATS (5)
EMNLP (2)
COLING (1)
CONLL (1)
IJCAI (1)
Top co-authors
Research topics
Keywords
determinantal point process
(6)
differential privacy
(5)
empirical risk minimization
(4)
low-rank approximation
(3)
determinantal point processes
(3)
spectral learning
(3)
quantile estimation
(2)
structured prediction
(2)
probabilistic model
(2)
singular value decomposition
(2)
statistical estimation
(2)
kernel matrix
(2)
matrix factorization
(2)
online learning
(2)
mean estimation
(2)
submodular optimization
(2)
approximate inference
(2)
probabilistic modeling
(2)
covariance matrix
(2)
recommender system
(2)
Papers
General Staircase Mechanisms for Optimal Differential Privacy
AISTATS 2025
Approximate Differential Privacy of the $\ell_2$ Mechanism
ICML 2025
Mean Estimation in the Add-Remove Model of Differential Privacy
ICML 2024
Subset-Based Instance Optimality in Private Estimation
ICML 2023
Learning with User-Level Privacy
NIPS 2021
Differentially Private Quantiles
ICML 2021
Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy
ICML 2019
Differentially Private Covariance Estimation
NIPS 2019
A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
ICML 2019
Completing State Representations using Spectral Learning
NIPS 2018
Maximizing Induced Cardinality Under a Determinantal Point Process
NIPS 2018
The Dependence of Effective Planning Horizon on Model Accuracy
IJCAI 2016
Low-Rank Spectral Learning with Weighted Loss Functions
AISTATS 2015
Abstraction Selection in Model-based Reinforcement Learning
ICML 2015
Low-Rank Spectral Learning
AISTATS 2014
Expectation-Maximization for Learning Determinantal Point Processes
NIPS 2014
NystrΓΆm Approximation for Large-Scale Determinantal Processes
AISTATS 2013
Discovering Diverse and Salient Threads in Document Collections
CONLL 2012
Discovering Diverse and Salient Threads in Document Collections
EMNLP 2012
Near-Optimal MAP Inference for Determinantal Point Processes
NIPS 2012
Structured Determinantal Point Processes
NIPS 2010
Exploiting Feature Covariance in High-Dimensional Online Learning
AISTATS 2010
Adaptive Regularization of Weight Vectors
NIPS 2009
Multi-Class Confidence Weighted Algorithms
EMNLP 2009
Structured Learning with Approximate Inference
NIPS 2007
Learning Bounds for Domain Adaptation
NIPS 2007
Confidence Estimation for Machine Translation
COLING 2004