Lester Mackey

Affiliations:
  • Microsoft Research
  • Stanford University, Department of Statistics


According to our database1, Lester Mackey authored at least 65 papers between 2006 and 2022.

Collaborative distances:

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Bibliography

2022
Gradient Estimation with Discrete Stein Operators.
CoRR, 2022

Scalable Spike-and-Slab.
Proceedings of the International Conference on Machine Learning, 2022

2021
Bounding Wasserstein distance with couplings.
CoRR, 2021

Distribution Compression in Near-linear Time.
CoRR, 2021

Generalized Kernel Thinning.
CoRR, 2021

Learned Benchmarks for Subseasonal Forecasting.
CoRR, 2021

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms.
CoRR, 2021

Near-optimal inference in adaptive linear regression.
CoRR, 2021

Sampling with Mirrored Stein Operators.
CoRR, 2021

Online Learning with Optimism and Delay.
Proceedings of the 38th International Conference on Machine Learning, 2021

Initialization and Regularization of Factorized Neural Layers.
Proceedings of the 9th International Conference on Learning Representations, 2021

Knowledge Distillation as Semiparametric Inference.
Proceedings of the 9th International Conference on Learning Representations, 2021

Kernel Thinning.
Proceedings of the Conference on Learning Theory, 2021

2020
Model-specific Data Subsampling with Influence Functions.
CoRR, 2020

Metrizing Weak Convergence with Maximum Mean Discrepancies.
CoRR, 2020

Weighted Meta-Learning.
CoRR, 2020

Stochastic Stein Discrepancies.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Minimax Estimation of Conditional Moment Models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Cross-validation Confidence Intervals for Test Error.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Single Point Transductive Prediction.
Proceedings of the 37th International Conference on Machine Learning, 2020

Approximate Cross-validation: Guarantees for Model Assessment and Selection.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Importance Sampling via Local Sensitivity.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Debiasing Linear Prediction.
CoRR, 2019

A Kernel Stein Test for Comparing Latent Variable Models.
CoRR, 2019

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond.
CoRR, 2019

Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Minimum Stein Discrepancy Estimators.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Improving Subseasonal Forecasting in the Western U.S. with Machine Learning.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Stein Point Markov Chain Monte Carlo.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
Model Compression with Generative Adversarial Networks.
CoRR, 2018

DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation.
CoRR, 2018

Random Feature Stein Discrepancies.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Global Non-convex Optimization with Discretized Diffusions.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Expert identification of visual primitives used by CNNs during mammogram classification.
Proceedings of the Medical Imaging 2018: Computer-Aided Diagnosis, 2018

Orthogonal Machine Learning: Power and Limitations.
Proceedings of the 35th International Conference on Machine Learning, 2018

Accurate Inference for Adaptive Linear Models.
Proceedings of the 35th International Conference on Machine Learning, 2018

Stein Points.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets.
J. Am. Medical Informatics Assoc., 2017

Improving Gibbs Sampler Scan Quality with DoGS.
Proceedings of the 34th International Conference on Machine Learning, 2017

Measuring Sample Quality with Kernels.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Measuring Sample Quality with Diffusions.
CoRR, 2016

Automated Organization of Electronic Health Record Data by Probabilistic Topic Modeling to Inform Clinical Decision Making.
Proceedings of the Summit on Clinical Research Informatics, 2016

2015
Combinatorial Clustering and the Beta Negative Binomial Process.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Distributed matrix completion and robust factorization.
J. Mach. Learn. Res., 2015

Measuring Sample Quality with Stein's Method.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Mixed Membership Matrix Factorization.
Proceedings of the Handbook of Mixed Membership Models and Their Applications., 2014

Corrupted Sensing: Novel Guarantees for Separating Structured Signals.
IEEE Trans. Inf. Theory, 2014

Joint Link Prediction and Attribute Inference Using a Social-Attribute Network.
ACM Trans. Intell. Syst. Technol., 2014

Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge.
CoRR, 2014

Weighted Classification Cascades for Optimizing Discovery Significance in the HiggsML Challenge.
Proceedings of the Workshop on High-energy Physics and Machine Learning, 2014

2013
Divide-and-Conquer Subspace Segmentation
CoRR, 2013

Distributed Low-Rank Subspace Segmentation.
Proceedings of the IEEE International Conference on Computer Vision, 2013

2012
Matrix Factorization and Matrix Concentration.
PhD thesis, 2012

The Asymptotics of Ranking Algorithms
CoRR, 2012

2011
Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)
CoRR, 2011

Divide-and-Conquer Matrix Factorization.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Visually Relating Gene Expression and in vivo DNA Binding Data.
Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2011

2010
Mixed Membership Matrix Factorization.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

On the Consistency of Ranking Algorithms.
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009
Feature-Weighted Linear Stacking
CoRR, 2009

2008
Deflation Methods for Sparse PCA.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
Fault-tolerant typed assembly language.
Proceedings of the ACM SIGPLAN 2007 Conference on Programming Language Design and Implementation, 2007

2006
Static typing for a faulty lambda calculus.
Proceedings of the 11th ACM SIGPLAN International Conference on Functional Programming, 2006

Participatory design with proxies: developing a desktop-PDA system to support people with aphasia.
Proceedings of the 2006 Conference on Human Factors in Computing Systems, 2006


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