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ORCIDLester Mackey
Lester W. Mackey
Person information
- affiliation: Microsoft Research
- affiliation: Stanford University, Department of Statistics
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2020 – today
- 2024
[j8]Raaz Dwivedi, Lester Mackey:
Kernel Thinning. J. Mach. Learn. Res. 25: 152:1-152:77 (2024)- [c47]Ayush Agrawal, Mirac Suzgun, Lester Mackey, Adam Kalai:
Do Language Models Know When They're Hallucinating References? EACL (Findings) 2024: 912-928 - [c46]Lingxiao Li, Raaz Dwivedi, Lester Mackey:
Debiased Distribution Compression. ICML 2024 - [i59]Lingxiao Li, Raaz Dwivedi, Lester Mackey:
Debiased Distribution Compression. CoRR abs/2404.12290 (2024) - [i58]Yixiu Zhao, Jiaxin Shi, Lester Mackey, Scott W. Linderman:
Informed Correctors for Discrete Diffusion Models. CoRR abs/2407.21243 (2024) - 2023
- [j7]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): 1858-1884 (2023) - [j6]Carl-Johann Simon-Gabriel, Alessandro Barp, Bernhard Schölkopf, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. J. Mach. Learn. Res. 24: 184:1-184:20 (2023) - [c45]Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey:
Compress Then Test: Powerful Kernel Testing in Near-linear Time. AISTATS 2023: 1174-1218 - [c44]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 - [c43]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 - [c42]Louis Sharrock, Lester Mackey, Christopher Nemeth:
Learning Rate Free Bayesian Inference in Constrained Domains. NeurIPS 2023 - [c41]Jiaxin Shi, Lester Mackey:
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. NeurIPS 2023 - [i57]Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey:
Compress Then Test: Powerful Kernel Testing in Near-linear Time. CoRR abs/2301.05974 (2023) - [i56]Louis Sharrock, Lester Mackey, Christopher Nemeth:
Learning Rate Free Bayesian Inference in Constrained Domains. CoRR abs/2305.14943 (2023) - [i55]Ayush Agrawal, Lester Mackey, Adam Tauman Kalai:
Do Language Models Know When They're Hallucinating References? CoRR abs/2305.18248 (2023) - [i54]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 abs/2306.11839 (2023) - [i53]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 abs/2307.08774 (2023) - [i52]Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai:
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation. CoRR abs/2310.02304 (2023) - [i51]Konstantin Klemmer, Esther Rolf, Caleb Robinson, Lester Mackey, Marc Rußwurm:
SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery. CoRR abs/2311.17179 (2023) - 2022
- [c40]Raaz Dwivedi, Lester Mackey:
Generalized Kernel Thinning. ICLR 2022 - [c39]Abhishek Shetty, Raaz Dwivedi, Lester Mackey:
Distribution Compression in Near-Linear Time. ICLR 2022 - [c38]Jiaxin Shi, Chang Liu, Lester Mackey:
Sampling with Mirrored Stein Operators. ICLR 2022 - [c37]Niloy Biswas, Lester Mackey, Xiao-Li Meng:
Scalable Spike-and-Slab. ICML 2022: 2021-2040 - [c36]Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey:
Gradient Estimation with Discrete Stein Operators. NeurIPS 2022 - [i50]Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey:
Gradient Estimation with Discrete Stein Operators. CoRR abs/2202.09497 (2022) - [i49]Niloy Biswas, Lester Mackey, Xiao-Li Meng:
Scalable Spike-and-Slab. CoRR abs/2204.01668 (2022) - [i48]Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey:
Adaptive Bias Correction for Improved Subseasonal Forecasting. CoRR abs/2209.10666 (2022) - [i47]Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami, Lester Mackey:
Targeted Separation and Convergence with Kernel Discrepancies. CoRR abs/2209.12835 (2022) - [i46]David Alvarez-Melis, Nicolò Fusi, Lester Mackey, Tal Wagner:
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport. CoRR abs/2210.13630 (2022) - [i45]Heishiro Kanagawa, Arthur Gretton, Lester Mackey:
Controlling Moments with Kernel Stein Discrepancies. CoRR abs/2211.05408 (2022) - [i44]Jiaxin Shi, Lester Mackey:
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. CoRR abs/2211.09721 (2022) - 2021
- [c35]Raaz Dwivedi, Lester Mackey:
Kernel Thinning. COLT 2021: 1753 - [c34]Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey:
Knowledge Distillation as Semiparametric Inference. ICLR 2021 - [c33]Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi:
Initialization and Regularization of Factorized Neural Layers. ICLR 2021 - [c32]Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey:
Online Learning with Optimism and Delay. ICML 2021: 3363-3373 - [i43]Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey:
Knowledge Distillation as Semiparametric Inference. CoRR abs/2104.09732 (2021) - [i42]Mikhail Khodak, Neil A. Tenenholtz, Lester Mackey, Nicolò Fusi:
Initialization and Regularization of Factorized Neural Layers. CoRR abs/2105.01029 (2021) - [i41]Raaz Dwivedi, Lester Mackey:
Kernel Thinning. CoRR abs/2105.05842 (2021) - [i40]Genevieve Flaspohler, Francesco Orabona, Judah Cohen, Soukayna Mouatadid, Miruna Oprescu, Paulo Orenstein, Lester Mackey:
Online Learning with Optimism and Delay. CoRR abs/2106.06885 (2021) - [i39]Jiaxin Shi, Chang Liu, Lester Mackey:
Sampling with Mirrored Stein Operators. CoRR abs/2106.12506 (2021) - [i38]Koulik Khamaru, Yash Deshpande, Lester Mackey, Martin J. Wainwright:
Near-optimal inference in adaptive linear regression. CoRR abs/2107.02266 (2021) - [i37]Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai:
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms. CoRR abs/2108.11056 (2021) - [i36]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 abs/2109.10399 (2021) - [i35]Raaz Dwivedi, Lester Mackey:
Generalized Kernel Thinning. CoRR abs/2110.01593 (2021) - [i34]Abhishek Shetty, Raaz Dwivedi, Lester Mackey:
Distribution Compression in Near-linear Time. CoRR abs/2111.07941 (2021) - [i33]Niloy Biswas, Lester Mackey:
Bounding Wasserstein distance with couplings. CoRR abs/2112.03152 (2021) - 2020
- [c31]Anant Raj, Cameron Musco, Lester Mackey:
Importance Sampling via Local Sensitivity. AISTATS 2020: 3099-3109 - [c30]Ashia C. Wilson, Maximilian Kasy, Lester Mackey:
Approximate Cross-validation: Guarantees for Model Assessment and Selection. AISTATS 2020: 4530-4540 - [c29]Nilesh Tripuraneni, Lester Mackey:
Single Point Transductive Prediction. ICML 2020: 9593-9602 - [c28]Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey:
Cross-validation Confidence Intervals for Test Error. NeurIPS 2020 - [c27]Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis:
Minimax Estimation of Conditional Moment Models. NeurIPS 2020 - [c26]Jackson Gorham, Anant Raj, Lester Mackey:
Stochastic Stein Discrepancies. NeurIPS 2020 - [i32]Ashia C. Wilson, Maximilian Kasy, Lester Mackey:
Approximate Cross-validation: Guarantees for Model Assessment and Selection. CoRR abs/2003.00617 (2020) - [i31]Diana Cai, Rishit Sheth, Lester Mackey, Nicoló Fusi:
Weighted Meta-Learning. CoRR abs/2003.09465 (2020) - [i30]Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis:
Minimax Estimation of Conditional Moment Models. CoRR abs/2006.07201 (2020) - [i29]Carl-Johann Simon-Gabriel, Alessandro Barp, Lester Mackey:
Metrizing Weak Convergence with Maximum Mean Discrepancies. CoRR abs/2006.09268 (2020) - [i28]Jackson Gorham, Anant Raj, Lester Mackey:
Stochastic Stein Discrepancies. CoRR abs/2007.02857 (2020) - [i27]Pierre Bayle, Alexandre Bayle, Lucas Janson, Lester Mackey:
Cross-validation Confidence Intervals for Test Error. CoRR abs/2007.12671 (2020) - [i26]Anant Raj, Cameron Musco, Lester Mackey, Nicoló Fusi:
Model-specific Data Subsampling with Influence Functions. CoRR abs/2010.10218 (2020)
2010 – 2019
- 2019
- [c25]Wilson Ye Chen, Alessandro Barp, François-Xavier Briol, Jackson Gorham, Mark A. Girolami, Lester W. Mackey, Chris J. Oates:
Stein Point Markov Chain Monte Carlo. ICML 2019: 1011-1021 - [c24]Jessica Hwang, Paulo Orenstein, Judah Cohen, Karl Pfeiffer, Lester Mackey:
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. KDD 2019: 2325-2335 - [c23]Xuechen Li, Yi Wu, Lester Mackey:
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. NeurIPS 2019: 7746-7758 - [c22]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. NeurIPS 2019: 12964-12976 - [c21]Ashia C. Wilson, Lester Mackey, Andre Wibisono:
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. NeurIPS 2019: 13533-13543 - [i25]Xuechen Li, Denny Wu, Lester Mackey, Murat A. Erdogdu:
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. CoRR abs/1906.07868 (2019) - [i24]Alessandro Barp, François-Xavier Briol, Andrew B. Duncan, Mark A. Girolami, Lester W. Mackey:
Minimum Stein Discrepancy Estimators. CoRR abs/1906.08283 (2019) - [i23]Heishiro Kanagawa, Wittawat Jitkrittum, Lester Mackey, Kenji Fukumizu, Arthur Gretton:
A Kernel Stein Test for Comparing Latent Variable Models. CoRR abs/1907.00586 (2019) - [i22]Nilesh Tripuraneni, Lester Mackey:
Debiasing Linear Prediction. CoRR abs/1908.02341 (2019) - [i21]Anant Raj, Cameron Musco, Lester Mackey:
Importance Sampling via Local Sensitivity. CoRR abs/1911.01575 (2019) - 2018
- [c20]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. ICML 2018: 843-852 - [c19]Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy:
Accurate Inference for Adaptive Linear Models. ICML 2018: 1202-1211 - [c18]Ilias Zadik, Lester W. Mackey, Vasilis Syrgkanis:
Orthogonal Machine Learning: Power and Limitations. ICML 2018: 5723-5731 - [c17]Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester W. Mackey, Genevieve Patterson:
Expert identification of visual primitives used by CNNs during mammogram classification. Medical Imaging: Computer-Aided Diagnosis 2018: 105752T - [c16]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. NeurIPS 2018: 1903-1913 - [c15]Murat A. Erdogdu, Lester Mackey, Ohad Shamir:
Global Non-convex Optimization with Discretized Diffusions. NeurIPS 2018: 9694-9703 - [i20]Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester W. Mackey, Genevieve Patterson:
Expert identification of visual primitives used by CNNs during mammogram classification. CoRR abs/1803.04858 (2018) - [i19]Wilson Ye Chen, Lester W. Mackey, Jackson Gorham, François-Xavier Briol, Chris J. Oates:
Stein Points. CoRR abs/1803.10161 (2018) - [i18]Jimmy Wu, Bolei Zhou, Diondra Peck, Scott Hsieh, Vandana Dialani, Lester W. Mackey, Genevieve Patterson:
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation. CoRR abs/1805.12323 (2018) - [i17]Jonathan H. Huggins, Lester Mackey:
Random Feature Stein Discrepancies. CoRR abs/1806.07788 (2018) - [i16]Jessica Hwang, Paulo Orenstein, Karl Pfeiffer, Judah Cohen, Lester Mackey:
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. CoRR abs/1809.07394 (2018) - [i15]Murat A. Erdogdu, Lester Mackey, Ohad Shamir:
Global Non-convex Optimization with Discretized Diffusions. CoRR abs/1810.12361 (2018) - [i14]Ruishan Liu, Nicoló Fusi, Lester Mackey:
Model Compression with Generative Adversarial Networks. CoRR abs/1812.02271 (2018) - 2017
- [j5]Jonathan H. Chen, Mary K. Goldstein, Steven M. Asch, Lester W. Mackey, Russ B. Altman:
Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets. J. Am. Medical Informatics Assoc. 24(3): 472-480 (2017) - [c14]Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Kernels. ICML 2017: 1292-1301 - [c13]Ioannis Mitliagkas, Lester W. Mackey:
Improving Gibbs Sampler Scan Quality with DoGS. ICML 2017: 2469-2477 - [i13]Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Kernels. CoRR abs/1703.01717 (2017) - [i12]Ioannis Mitliagkas, Lester W. Mackey:
Improving Gibbs Sampler Scan Quality with DoGS. CoRR abs/1707.05807 (2017) - [i11]Lester W. Mackey, Vasilis Syrgkanis, Ilias Zadik:
Orthogonal Machine Learning: Power and Limitations. CoRR abs/1711.00342 (2017) - [i10]Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy:
Accurate Inference for Adaptive Linear Models. CoRR abs/1712.06695 (2017) - 2016
- [c12]Jonathan H. Chen, Mary K. Goldstein, Steven M. Asch, Lester W. Mackey, Russ B. Altman:
Automated Organization of Electronic Health Record Data by Probabilistic Topic Modeling to Inform Clinical Decision Making. CRI 2016 - [i9]Jack Gorham, Andrew B. Duncan, Sebastian J. Vollmer, Lester W. Mackey:
Measuring Sample Quality with Diffusions. CoRR abs/1611.06972 (2016) - 2015
- [j4]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Distributed matrix completion and robust factorization. J. Mach. Learn. Res. 16: 913-960 (2015) - [j3]Tamara Broderick, Lester W. Mackey, John W. Paisley, Michael I. Jordan:
Combinatorial Clustering and the Beta Negative Binomial Process. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 290-306 (2015) - [c11]Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Stein's Method. NIPS 2015: 226-234 - [i8]Jackson Gorham, Lester W. Mackey:
Measuring Sample Quality with Stein's Method. CoRR abs/1506.03039 (2015) - 2014
- [j2]Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song:
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. ACM Trans. Intell. Syst. Technol. 5(2): 27:1-27:20 (2014) - [j1]Rina Foygel, Lester W. Mackey:
Corrupted Sensing: Novel Guarantees for Separating Structured Signals. IEEE Trans. Inf. Theory 60(2): 1223-1247 (2014) - [c10]Lester W. Mackey, Jordan Bryan, Man Yue Mo:
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge. HEPML@NIPS 2014: 129-134 - [r1]Lester W. Mackey, David J. Weiss, Michael I. Jordan:
Mixed Membership Matrix Factorization. Handbook of Mixed Membership Models and Their Applications 2014: 351-367 - [i7]Lester W. Mackey, Jordan Bryan:
Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge. CoRR abs/1409.2655 (2014) - 2013
- [c9]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Distributed Low-Rank Subspace Segmentation. ICCV 2013: 3543-3550 - [i6]Ameet Talwalkar, Lester W. Mackey, Yadong Mu, Shih-Fu Chang, Michael I. Jordan:
Divide-and-Conquer Subspace Segmentation. CoRR abs/1304.5583 (2013) - [i5]Rina Foygel, Lester W. Mackey:
Corrupted Sensing: Novel Guarantees for Separating Structured Signals. CoRR abs/1305.2524 (2013) - 2012
- [b1]Lester Mackey:
Matrix Factorization and Matrix Concentration. University of California, Berkeley, USA, 2012 - [i4]John C. Duchi, Lester W. Mackey, Michael I. Jordan:
The Asymptotics of Ranking Algorithms. CoRR abs/1204.1688 (2012) - 2011
- [c8]Min-Yu Huang, Lester W. Mackey, Soile V. E. Keränen, Gunther H. Weber, Michael I. Jordan, David W. Knowles, Mark D. Biggin, Bernd Hamann:
Visually Relating Gene Expression and in vivo DNA Binding Data. BIBM 2011: 586-589 - [c7]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Divide-and-Conquer Matrix Factorization. NIPS 2011: 1134-1142 - [i3]Lester W. Mackey, Ameet Talwalkar, Michael I. Jordan:
Divide-and-Conquer Matrix Factorization. CoRR abs/1107.0789 (2011) - [i2]Neil Zhenqiang Gong, Ameet Talwalkar, Lester W. Mackey, Ling Huang, Eui Chul Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song:
Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN). CoRR abs/1112.3265 (2011) - 2010
- [c6]John C. Duchi, Lester W. Mackey, Michael I. Jordan:
On the Consistency of Ranking Algorithms. ICML 2010: 327-334 - [c5]Lester W. Mackey, David J. Weiss, Michael I. Jordan:
Mixed Membership Matrix Factorization. ICML 2010: 711-718
2000 – 2009
- 2009
- [i1]Joseph Sill, Gábor Takács, Lester W. Mackey, David Lin:
Feature-Weighted Linear Stacking. CoRR abs/0911.0460 (2009) - 2008
- [c4]Lester W. Mackey:
Deflation Methods for Sparse PCA. NIPS 2008: 1017-1024 - 2007
- [c3]Frances Perry, Lester W. Mackey, George A. Reis, Jay Ligatti, David I. August, David Walker:
Fault-tolerant typed assembly language. PLDI 2007: 42-53 - 2006
- [c2]Jordan L. Boyd-Graber, Sonya S. Nikolova, Karyn Moffatt, Kenrick C. Kin, Joshua Y. Lee, Lester W. Mackey, Marilyn Tremaine, Maria M. Klawe:
Participatory design with proxies: developing a desktop-PDA system to support people with aphasia. CHI 2006: 151-160 - [c1]David Walker, Lester W. Mackey, Jay Ligatti, George A. Reis, David I. August:
Static typing for a faulty lambda calculus. ICFP 2006: 38-49
Coauthor Index
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