Lester Mackey

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 70

Top Topics

Gradient Estimation

Perceptron Learning

Top Venues

Publications

Lingxiao Li, Raaz Dwivedi, Lester Mackey
Debiased Distribution Compression. CoRR (2024)
Yixiu Zhao, Jiaxin Shi, Lester Mackey, Scott W. Linderman
Informed Correctors for Discrete Diffusion Models. CoRR (2024)
Ayush Agrawal, Mirac Suzgun, Lester Mackey, Adam Kalai
Do Language Models Know When They're Hallucinating References? EACL (Findings) (2024)
Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai
Social norm bias: residual harms of fairness-aware algorithms. Data Min. Knowl. Discov. 37 (5) (2023)
Ayush Agrawal, Lester Mackey, Adam Tauman Kalai
Do Language Models Know When They're Hallucinating References? CoRR (2023)
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, Lester Mackey
SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking. NeurIPS (2023)
Louis Sharrock, Lester Mackey, Christopher Nemeth
Learning Rate Free Bayesian Inference in Constrained Domains. NeurIPS (2023)
Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey
Metrizing Weak Convergence with Maximum Mean Discrepancies. J. Mach. Learn. Res. 24 (2023)
Jiaxin Shi, Lester Mackey
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. NeurIPS (2023)
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Compress Then Test: Powerful Kernel Testing in Near-linear Time. CoRR (2023)
Hammaad Adam, Fan Yin, Huibin Hu, Neil A. Tenenholtz, Lorin Crawford, Lester Mackey, Allison Koenecke
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations. NeurIPS (2023)
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Compress Then Test: Powerful Kernel Testing in Near-linear Time. AISTATS (2023)
Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery. CoRR (2023)
Louis Sharrock, Lester Mackey, Christopher Nemeth
Learning Rate Free Bayesian Inference in Constrained Domains. CoRR (2023)
Hammaad Adam, Fan Yin, Mary Hu, Neil A. Tenenholtz, Lorin Crawford, Lester Mackey, Allison Koenecke
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations. CoRR (2023)
Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation. CoRR (2023)
Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya Y. Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa S. Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey, Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David Thau, Milind Tambe
Reflections from the Workshop on AI-Assisted Decision Making for Conservation. CoRR (2023)
David Alvarez-Melis, Nicolò Fusi, Lester Mackey, Tal Wagner
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport. CoRR (2022)
Raaz Dwivedi, Lester Mackey
Generalized Kernel Thinning. ICLR (2022)
Niloy Biswas, Lester Mackey, Xiao-Li Meng
Scalable Spike-and-Slab. CoRR (2022)
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey
Gradient Estimation with Discrete Stein Operators. CoRR (2022)
Heishiro Kanagawa, Arthur Gretton, Lester Mackey
Controlling Moments with Kernel Stein Discrepancies. CoRR (2022)
Jiaxin Shi, Chang Liu, Lester Mackey
Sampling with Mirrored Stein Operators. ICLR (2022)
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey
Adaptive Bias Correction for Improved Subseasonal Forecasting. CoRR (2022)
Niloy Biswas, Lester Mackey, Xiao-Li Meng
Scalable Spike-and-Slab. ICML (2022)
Abhishek Shetty, Raaz Dwivedi, Lester Mackey
Distribution Compression in Near-Linear Time. ICLR (2022)
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey
Gradient Estimation with Discrete Stein Operators. NeurIPS (2022)
Jiaxin Shi, Lester Mackey
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. CoRR (2022)
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey
Targeted Separation and Convergence with Kernel Discrepancies. CoRR (2022)
Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi
Initialization and Regularization of Factorized Neural Layers. CoRR (2021)
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, Lester Mackey
Learned Benchmarks for Subseasonal Forecasting. CoRR (2021)
Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey
Online Learning with Optimism and Delay. CoRR (2021)
Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi
Initialization and Regularization of Factorized Neural Layers. ICLR (2021)
Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey
Knowledge Distillation as Semiparametric Inference. ICLR (2021)
Raaz Dwivedi, Lester Mackey
Generalized Kernel Thinning. CoRR (2021)
Abhishek Shetty, Raaz Dwivedi, Lester Mackey
Distribution Compression in Near-linear Time. CoRR (2021)
Raaz Dwivedi, Lester Mackey
Kernel Thinning. COLT (2021)
Raaz Dwivedi, Lester Mackey
Kernel Thinning. CoRR (2021)
Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. CoRR (2021)
Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey
Online Learning with Optimism and Delay. ICML (2021)
Jiaxin Shi, Chang Liu, Lester Mackey
Sampling with Mirrored Stein Operators. CoRR (2021)
Niloy Biswas, Lester Mackey
Bounding Wasserstein distance with couplings. CoRR (2021)
Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey
Knowledge Distillation as Semiparametric Inference. CoRR (2021)
Koulik Khamaru, Yash Deshpande, Lester Mackey, Martin J. Wainwright
Near-optimal inference in adaptive linear regression. CoRR (2021)
Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey
Cross-validation Confidence Intervals for Test Error. CoRR (2020)
Jackson Gorham, Anant Raj, Lester Mackey
Stochastic Stein Discrepancies. NeurIPS (2020)
Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
Minimax Estimation of Conditional Moment Models. CoRR (2020)
Ashia C. Wilson, Maximilian Kasy, Lester Mackey
Approximate Cross-validation: Guarantees for Model Assessment and Selection. AISTATS (2020)
Ashia C. Wilson, Maximilian Kasy, Lester Mackey
Approximate Cross-validation: Guarantees for Model Assessment and Selection. CoRR (2020)
Anant Raj, Cameron Musco, Lester Mackey
Importance Sampling via Local Sensitivity. AISTATS (2020)
Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey
Cross-validation Confidence Intervals for Test Error. NeurIPS (2020)
Jackson Gorham, Anant Raj, Lester Mackey
Stochastic Stein Discrepancies. CoRR (2020)
Carl-Johann Simon-Gabriel, Alessandro Barp, Lester Mackey
Metrizing Weak Convergence with Maximum Mean Discrepancies. CoRR (2020)
Nilesh Tripuraneni, Lester Mackey
Single Point Transductive Prediction. ICML (2020)
Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
Minimax Estimation of Conditional Moment Models. NeurIPS (2020)
Anant Raj, Cameron Musco, Lester Mackey, Nicoló Fusi
Model-specific Data Subsampling with Influence Functions. CoRR (2020)
Diana Cai, Rishit Sheth, Lester Mackey, Nicoló Fusi
Weighted Meta-Learning. CoRR (2020)
Xuechen Li, Yi Wu, Lester Mackey
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. NeurIPS (2019)
Anant Raj, Cameron Musco, Lester Mackey
Importance Sampling via Local Sensitivity. CoRR (2019)
Ashia C. Wilson, Lester Mackey, Andre Wibisono
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. NeurIPS (2019)
Jessica Hwang, Paulo Orenstein, Judah Cohen, Karl Pfeiffer, Lester Mackey
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. KDD (2019)
Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton
A Kernel Stein Test for Comparing Latent Variable Models. CoRR (2019)
Nilesh Tripuraneni, Lester Mackey
Debiasing Linear Prediction. CoRR (2019)
Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. CoRR (2019)
Jonathan H. Huggins, Lester Mackey
Random Feature Stein Discrepancies. NeurIPS (2018)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. CoRR (2018)
Ruishan Liu, Nicoló Fusi, Lester Mackey
Model Compression with Generative Adversarial Networks. CoRR (2018)
Jonathan H. Huggins, Lester Mackey
Random Feature Stein Discrepancies. CoRR (2018)
Jessica Hwang, Paulo Orenstein, Karl Pfeiffer, Judah Cohen, Lester Mackey
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. CoRR (2018)
Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Global Non-convex Optimization with Discretized Diffusions. NeurIPS (2018)