← Authors

Alex Kulesza

27 papers · 2004–2025 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) πŸ—ΊοΈ Taxonomy Completionist (17) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (21) 🌟 Keyword Trendsetter Combo (3) 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ“ˆ Trend Setter πŸ’Ž Century Club (27) πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (98) πŸ”₯ Unstoppable (5)

Conferences

NIPS (10) ICML (7) AISTATS (5) EMNLP (2) COLING (1) CONLL (1) IJCAI (1)

Top co-authors

Ben Taskar (6) Jennifer Gillenwater (5) Satinder Singh (5) Koby Crammer (4) Ananda Theertha Suresh (4) Nan Jiang (4) Kareem Amin (3) Fernando Pereira (3) Mark Dredze (3) Yuyan Wang (2)

Research topics

Differential Privacy (1) Statistics (1)

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