Participatory design with proxies: developing a desktop-PDA system to support people with aphasia. - Jordan L. Boyd-Graber; Sonya S. Nikolova; Karyn Moffatt; Kenrick C. Kin; Joshua Y. Lee; Lester W. Mackey; Marilyn Tremaine; Maria M. Klawe (CHI, 2006)

Static typing for a faulty lambda calculus. - David Walker 001; Lester W. Mackey; Jay Ligatti; George A. Reis; David I. August (ICFP, 2006)

Fault-tolerant typed assembly language. - Frances Perry; Lester W. Mackey; George A. Reis; Jay Ligatti; David I. August; David Walker 001 (PLDI, 2007)

Deflation Methods for Sparse PCA. - Lester W. Mackey (NIPS, 2008)

Feature-Weighted Linear Stacking - Joseph Sill; Gábor Takács; Lester W. Mackey; David Lin (CoRR, 2009)

On the Consistency of Ranking Algorithms. - John C. Duchi; Lester W. Mackey; Michael I. Jordan (ICML, 2010)

Mixed Membership Matrix Factorization. - Lester W. Mackey; David J. Weiss; Michael I. Jordan (ICML, 2010)

Visually Relating Gene Expression and in vivo DNA Binding Data. - Min-Yu Huang; Lester W. Mackey; Soile V. E. Keränen; Gunther H. Weber; Michael I. Jordan; David W. Knowles; Mark D. Biggin; Bernd Hamann (BIBM, 2011)

Divide-and-Conquer Matrix Factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (NIPS, 2011)

Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN) - Neil Zhenqiang Gong; Ameet Talwalkar; Lester W. Mackey; Ling Huang 001; Eui Chul Richard Shin; Emil Stefanov; Elaine Shi; Dawn Song (CoRR, 2011)

The Asymptotics of Ranking Algorithms - John C. Duchi; Lester W. Mackey; Michael I. Jordan (CoRR, 2012)

Matrix Factorization and Matrix Concentration. - Lester Mackey (2012)

Distributed Low-Rank Subspace Segmentation. - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (ICCV, 2013)

Divide-and-Conquer Subspace Segmentation - Ameet Talwalkar; Lester W. Mackey; Yadong Mu; Shih-Fu Chang; Michael I. Jordan (CoRR, 2013)

Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge. - Lester W. Mackey; Jordan Bryan; Man Yue Mo (HEPML@NIPS, 2014)

Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge. - Lester W. Mackey; Jordan Bryan (CoRR, 2014)

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network. - Neil Zhenqiang Gong; Ameet Talwalkar; Lester W. Mackey; Ling Huang 001; Eui Chul Richard Shin; Emil Stefanov; Elaine Shi; Dawn Song (ACM Trans. Intell. Syst. Technol., 2014)

Corrupted Sensing: Novel Guarantees for Separating Structured Signals. - Rina Foygel; Lester W. Mackey (IEEE Trans. Inf. Theory, 2014)

Mixed Membership Matrix Factorization. - Lester W. Mackey; David J. Weiss; Michael I. Jordan (Handbook of Mixed Membership Models and Their Applications, 2014)

Measuring Sample Quality with Stein's Method. - Jackson Gorham; Lester W. Mackey (NIPS, 2015)

Distributed matrix completion and robust factorization. - Lester W. Mackey; Ameet Talwalkar; Michael I. Jordan (J. Mach. Learn. Res., 2015)

Combinatorial Clustering and the Beta Negative Binomial Process. - Tamara Broderick; Lester W. Mackey; John W. Paisley; Michael I. Jordan (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Automated Organization of Electronic Health Record Data by Probabilistic Topic Modeling to Inform Clinical Decision Making. - Jonathan H. Chen; Mary K. Goldstein; Steven M. Asch; Lester W. Mackey; Russ B. Altman (CRI, 2016)

Measuring Sample Quality with Diffusions. - Jack Gorham; Andrew B. Duncan; Sebastian J. Vollmer; Lester W. Mackey (CoRR, 2016)

Measuring Sample Quality with Kernels. - Jackson Gorham; Lester W. Mackey (ICML, 2017)

Improving Gibbs Sampler Scan Quality with DoGS. - Ioannis Mitliagkas; Lester W. Mackey (ICML, 2017)

Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets. - Jonathan H. Chen; Mary K. Goldstein; Steven M. Asch; Lester W. Mackey; Russ B. Altman (J. Am. Medical Informatics Assoc., 2017)

Stein Points. - Wilson Ye Chen; Lester W. Mackey; Jackson Gorham; François-Xavier Briol; Chris J. Oates (ICML, 2018)

Accurate Inference for Adaptive Linear Models. - Yash Deshpande; Lester W. Mackey; Vasilis Syrgkanis; Matt Taddy (ICML, 2018)

Orthogonal Machine Learning: Power and Limitations. - Ilias Zadik; Lester W. Mackey; Vasilis Syrgkanis (ICML, 2018)

Expert identification of visual primitives used by CNNs during mammogram classification. - Jimmy Wu; Diondra Peck; Scott Hsieh; Vandana Dialani; Constance D. Lehman; Bolei Zhou; Vasilis Syrgkanis; Lester W. Mackey; Genevieve Patterson (Computer-Aided Diagnosis, 2018)

Global Non-convex Optimization with Discretized Diffusions. - Murat A. Erdogdu; Lester Mackey; Ohad Shamir (NeurIPS, 2018)

Random Feature Stein Discrepancies. - Jonathan H. Huggins; Lester Mackey (NeurIPS, 2018)

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation. - Jimmy Wu; Bolei Zhou; Diondra Peck; Scott Hsieh; Vandana Dialani; Lester W. Mackey; Genevieve Patterson (CoRR, 2018)

Model Compression with Generative Adversarial Networks. - Ruishan Liu; Nicoló Fusi; Lester Mackey (CoRR, 2018)

Stein Point Markov Chain Monte Carlo. - Wilson Ye Chen; Alessandro Barp; François-Xavier Briol; Jackson Gorham; Mark A. Girolami; Lester W. Mackey; Chris J. Oates (ICML, 2019)

Improving Subseasonal Forecasting in the Western U.S. with Machine Learning. - Jessica Hwang; Paulo Orenstein; Judah Cohen; Karl Pfeiffer; Lester Mackey (KDD, 2019)

Minimum Stein Discrepancy Estimators. - Alessandro Barp; François-Xavier Briol; Andrew B. Duncan; Mark A. Girolami; Lester W. Mackey (NeurIPS, 2019)

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. - Xuechen Li; Yi Wu; Lester Mackey (NeurIPS, 2019)

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions. - Ashia C. Wilson; Lester Mackey; Andre Wibisono (NeurIPS, 2019)

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond. - Xuechen Li; Denny Wu; Lester Mackey; Murat A. Erdogdu (CoRR, 2019)

A Kernel Stein Test for Comparing Latent Variable Models. - Heishiro Kanagawa; Wittawat Jitkrittum; Lester Mackey; Kenji Fukumizu; Arthur Gretton (CoRR, 2019)

Debiasing Linear Prediction. - Nilesh Tripuraneni; Lester Mackey (CoRR, 2019)

Importance Sampling via Local Sensitivity. - Anant Raj; Cameron Musco; Lester Mackey (AISTATS, 2020)

Approximate Cross-validation: Guarantees for Model Assessment and Selection. - Ashia C. Wilson; Maximilian Kasy; Lester Mackey (AISTATS, 2020)

Single Point Transductive Prediction. - Nilesh Tripuraneni; Lester Mackey (ICML, 2020)

Cross-validation Confidence Intervals for Test Error. - Pierre Bayle; Alexandre Bayle; Lucas Janson; Lester Mackey (NeurIPS, 2020)

Minimax Estimation of Conditional Moment Models. - Nishanth Dikkala; Greg Lewis; Lester Mackey; Vasilis Syrgkanis (NeurIPS, 2020)

Stochastic Stein Discrepancies. - Jackson Gorham; Anant Raj; Lester Mackey (NeurIPS, 2020)

Weighted Meta-Learning. - Diana Cai; Rishit Sheth; Lester Mackey; Nicoló Fusi (CoRR, 2020)

Metrizing Weak Convergence with Maximum Mean Discrepancies. - Carl-Johann Simon-Gabriel; Alessandro Barp; Lester Mackey (CoRR, 2020)

Model-specific Data Subsampling with Influence Functions. - Anant Raj; Cameron Musco; Lester Mackey; Nicoló Fusi (CoRR, 2020)

Knowledge Distillation as Semiparametric Inference. - Tri Dao; Govinda M. Kamath; Vasilis Syrgkanis; Lester Mackey (ICLR, 2021)

Initialization and Regularization of Factorized Neural Layers. - Mikhail Khodak; Neil A. Tenenholtz; Lester Mackey; Nicolò Fusi (ICLR, 2021)

Online Learning with Optimism and Delay. - Genevieve Flaspohler; Francesco Orabona; Judah Cohen; Soukayna Mouatadid; Miruna Oprescu; Paulo Orenstein; Lester Mackey (ICML, 2021)

Near-optimal inference in adaptive linear regression. - Koulik Khamaru; Yash Deshpande; Lester Mackey; Martin J. Wainwright (CoRR, 2021)

Learned Benchmarks for Subseasonal Forecasting. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Miruna Oprescu; Judah Cohen; Franklyn Wang; Sean Knight; Maria Geogdzhayeva; Sam Levang; Ernest Fraenkel; Lester Mackey (CoRR, 2021)

Bounding Wasserstein distance with couplings. - Niloy Biswas; Lester Mackey (CoRR, 2021)

Generalized Kernel Thinning. - Raaz Dwivedi; Lester Mackey (ICLR, 2022)

Distribution Compression in Near-Linear Time. - Abhishek Shetty; Raaz Dwivedi; Lester Mackey (ICLR, 2022)

Sampling with Mirrored Stein Operators. - Jiaxin Shi; Chang Liu 030; Lester Mackey (ICLR, 2022)

Scalable Spike-and-Slab. - Niloy Biswas; Lester Mackey; Xiao-Li Meng (ICML, 2022)

Gradient Estimation with Discrete Stein Operators. - Jiaxin Shi; Yuhao Zhou; Jessica Hwang; Michalis K. Titsias; Lester Mackey (NeurIPS, 2022)

Adaptive Bias Correction for Improved Subseasonal Forecasting. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Judah Cohen; Miruna Oprescu; Ernest Fraenkel; Lester Mackey (CoRR, 2022)

Targeted Separation and Convergence with Kernel Discrepancies. - Alessandro Barp; Carl-Johann Simon-Gabriel; Mark Girolami; Lester Mackey (CoRR, 2022)

Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport. - David Alvarez-Melis; Nicolò Fusi; Lester Mackey; Tal Wagner (CoRR, 2022)

Controlling Moments with Kernel Stein Discrepancies. - Heishiro Kanagawa; Arthur Gretton; Lester Mackey (CoRR, 2022)

Compress Then Test: Powerful Kernel Testing in Near-linear Time. - Carles Domingo-Enrich; Raaz Dwivedi; Lester Mackey (AISTATS, 2023)

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations. - Hammaad Adam; Fan Yin; Huibin Hu; Neil A. Tenenholtz; Lorin Crawford; Lester Mackey; Allison Koenecke (NeurIPS, 2023)

SubseasonalClimateUSA: A Dataset for Subseasonal Forecasting and Benchmarking. - Soukayna Mouatadid; Paulo Orenstein; Genevieve Flaspohler; Miruna Oprescu; Judah Cohen; Franklyn Wang; Sean Knight; Maria Geogdzhayeva; Sam Levang; Ernest Fraenkel; Lester Mackey (NeurIPS, 2023)

Learning Rate Free Bayesian Inference in Constrained Domains. - Louis Sharrock; Lester Mackey; Christopher Nemeth (NeurIPS, 2023)

A Finite-Particle Convergence Rate for Stein Variational Gradient Descent. - Jiaxin Shi; Lester Mackey (NeurIPS, 2023)

Evaluation of Dependency Structure for Multivariate Weather Predictors Using Copulas. - Samuel C. Maina; Dorcas Gicuku Mwigereri; Jonathan A. Weyn; Lester Mackey; Millicent Ochieng (ACM J. Comput. Sustain. Soc., 2023)

Do Language Models Know When They're Hallucinating References? - Ayush Agrawal; Lester Mackey; Adam Tauman Kalai (CoRR, 2023)

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations. - Hammaad Adam; Fan Yin; Mary Hu; Neil A. Tenenholtz; Lorin Crawford; Lester Mackey; Allison Koenecke (CoRR, 2023)

Reflections from the Workshop on AI-Assisted Decision Making for Conservation. - 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 001; Andrew Davies; Nikhil Garg; et al. (CoRR, 2023)

Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation. - Eric Zelikman; Eliana Lorch; Lester Mackey; Adam Tauman Kalai (CoRR, 2023)

Metrizing Weak Convergence with Maximum Mean Discrepancies. - Carl-Johann Simon-Gabriel; Alessandro Barp; Bernhard Schölkopf; Lester Mackey (J. Mach. Learn. Res., 2023)

Social norm bias: residual harms of fairness-aware algorithms. - Myra Cheng; Maria De-Arteaga; Lester Mackey; Adam Tauman Kalai (Data Min. Knowl. Discov., 2023)

Do Language Models Know When They're Hallucinating References? - Ayush Agrawal; Mirac Suzgun; Lester Mackey; Adam Kalai (EACL (Findings), 2024)

Debiased Distribution Compression. - Lingxiao Li; Raaz Dwivedi; Lester Mackey (ICML, 2024)

SureMap: Simultaneous mean estimation for single-task and multi-task disaggregated evaluation. - Misha Khodak; Lester Mackey; Alexandra Chouldechova; Miro Dudík (NeurIPS, 2024)

Informed Correctors for Discrete Diffusion Models. - Yixiu Zhao; Jiaxin Shi; Lester Mackey; Scott W. Linderman (CoRR, 2024)

Adapting Language Models via Token Translation. - Zhili Feng; Tanya Marwah; Nicolò Fusi; David Alvarez-Melis; Lester Mackey (CoRR, 2024)

SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task Disaggregated Evaluation. - Mikhail Khodak; Lester Mackey; Alexandra Chouldechova; Miroslav Dudík (CoRR, 2024)

Kernel Thinning. - Raaz Dwivedi; Lester Mackey (J. Mach. Learn. Res., 2024)

SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagery. - Konstantin Klemmer; Esther Rolf; Caleb Robinson; Lester Mackey; Marc Rußwurm (AAAI, 2025)

Low-Rank Thinning. - Annabelle Michael Carrell; Albert Gong; Abhishek Shetty; Raaz Dwivedi; Lester Mackey (CoRR, 2025)

Causal integration of chemical structures improves representations of microscopy images for morphological profiling. - Yemin Yu; Neil A. Tenenholtz; Lester Mackey; Ying Wei; David Alvarez-Melis; Ava P. Amini; Alex X. Lu (CoRR, 2025)

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation. - Jikai Jin; Lester Mackey; Vasilis Syrgkanis (CoRR, 2025)

Estimating Treatment Effects with Independent Component Analysis. - Patrik Reizinger; Lester Mackey; Wieland Brendel; Rahul Krishnan (CoRR, 2025)