← Authors

Lester Mackey

34 papers · 2013–2025 · 9 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (12) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (53) πŸƒ Academic Marathon (12) 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ—ƒοΈ Keyword Collector (131) ⚑ Prolific Year (5) πŸš€ Conference Pioneer πŸ’Ž Century Club (34) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter ❓ The Questioner

Conferences

ICML (11) NIPS (7) ICLR (5) JMLR (4) AISTATS (3) AAAI (1) COLT (1) EACL (1) ICCV (1)

Top co-authors

Raaz Dwivedi (7) Jackson Gorham (4) Alessandro Barp (4) Vasilis Syrgkanis (3) Mark Girolami (3) François-Xavier Briol (3) Mikhail Khodak (2) Murat A Erdogdu (2) Wilson Ye Chen (2) Abhishek Shetty (2)

Research topics

Probability (1)

Keywords

markov chain monte carlo (8) kernel methods (5) stein discrepancy (5) maximum mean discrepancy (5) weak convergence (3) stein method (3) kernel stein discrepancy (3) reproducing kernel hilbert space (3) matrix factorization (2) convex optimization (2) sample quality (2) convergence rate (2) non-convex optimization (2) integration error (2) gibbs sampling (2) importance sampling (2) reproducing kernel (2) posterior inference (2) distributed computing (2) quasi-monte carlo (2)

Papers

SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery AAAI 2025
Low-Rank Thinning ICML 2025
Kernel Thinning JMLR 2024
Debiased Distribution Compression ICML 2024
SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation NIPS 2024
Do Language Models Know When They’re Hallucinating References? EACL 2024
Targeted Separation and Convergence with Kernel Discrepancies JMLR 2024
Metrizing Weak Convergence with Maximum Mean Discrepancies JMLR 2023
Compress Then Test: Powerful Kernel Testing in Near-linear Time AISTATS 2023
Generalized Kernel Thinning ICLR 2022
Distribution Compression in Near-Linear Time ICLR 2022
Sampling with Mirrored Stein Operators ICLR 2022
Scalable Spike-and-Slab ICML 2022
Online Learning with Optimism and Delay ICML 2021
Knowledge Distillation as Semiparametric Inference ICLR 2021
Kernel Thinning COLT 2021
Initialization and Regularization of Factorized Neural Layers ICLR 2021
Importance Sampling via Local Sensitivity AISTATS 2020
Approximate Cross-validation: Guarantees for Model Assessment and Selection AISTATS 2020
Single Point Transductive Prediction ICML 2020
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
Minimum Stein Discrepancy Estimators NIPS 2019
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions NIPS 2019
Stein Point Markov Chain Monte Carlo ICML 2019
Orthogonal Machine Learning: Power and Limitations ICML 2018
Global Non-convex Optimization with Discretized Diffusions NIPS 2018
Stein Points ICML 2018
Accurate Inference for Adaptive Linear Models ICML 2018
Random Feature Stein Discrepancies NIPS 2018
Improving Gibbs Sampler Scan Quality with DoGS ICML 2017
Measuring Sample Quality with Kernels ICML 2017
Distributed Matrix Completion and Robust Factorization JMLR 2015
Measuring Sample Quality with Stein's Method NIPS 2015
Distributed Low-Rank Subspace Segmentation ICCV 2013