
Learned Benchmarks for Subseasonal Forecasting
We develop a subseasonal forecasting toolkit of simple learned benchmark...
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Social Norm Bias: Residual Harms of FairnessAware Algorithms
Many modern learning algorithms mitigate bias by enforcing fairness acro...
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Nearoptimal inference in adaptive linear regression
When data is collected in an adaptive manner, even simple methods like o...
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Sampling with Mirrored Stein Operators
We introduce a new family of particle evolution samplers suitable for co...
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Kernel Thinning
We introduce kernel thinning, a new procedure for compressing a distribu...
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Stein's Method Meets Statistics: A Review of Some Recent Developments
Stein's method is a collection of tools for analysing distributional com...
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Initialization and Regularization of Factorized Neural Layers
Factorized layers–operations parameterized by products of two or more ma...
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Knowledge Distillation as Semiparametric Inference
A popular approach to model compression is to train an inexpensive stude...
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Modelspecific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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Independent finite approximations for Bayesian nonparametric inference: construction, error bounds, and practical implications
Bayesian nonparametrics based on completely random measures (CRMs) offer...
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Crossvalidation Confidence Intervals for Test Error
This work develops central limit theorems for crossvalidation and consi...
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Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and nonconvergence in app...
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Metrizing Weak Convergence with Maximum Mean Discrepancies
Theorem 12 of SimonGabriel Schölkopf (JMLR, 2018) seemed to close a...
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Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional m...
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Optimal Thinning of MCMC Output
The use of heuristics to assess the convergence and compress the output ...
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Weighted MetaLearning
Metalearning leverages related source tasks to learn an initialization ...
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Approximate Crossvalidation: Guarantees for Model Assessment and Selection
Crossvalidation (CV) is a popular approach for assessing and selecting ...
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Importance Sampling via Local Sensitivity
Given a loss function F:X→R^+ that can be written as the sum of losses o...
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Debiasing Linear Prediction
Standard methods in supervised learning separate training and prediction...
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A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernelbased test to assess the relative goo...
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Minimum Stein Discrepancy Estimators
When maximum likelihood estimation is infeasible, one often turns to sco...
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Stochastic RungeKutta Accelerates Langevin Monte Carlo and Beyond
Sampling with Markov chain Monte Carlo methods typically amounts to disc...
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Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
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Model Compression with Generative Adversarial Networks
More accurate machine learning models often demand more computation and ...
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Global Nonconvex Optimization with Discretized Diffusions
An Euler discretization of the Langevin diffusion is known to converge t...
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Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Water managers in the western United States (U.S.) rely on longterm fore...
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Random Feature Stein Discrepancies
Computable Stein discrepancies have been deployed for a variety of appli...
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DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
We propose DeepMiner, a framework to discover interpretable representati...
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Stein Points
An important task in computational statistics and machine learning is to...
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Expert identification of visual primitives used by CNNs during mammogram classification
This work interprets the internal representations of deep neural network...
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Accurate Inference for Adaptive Linear Models
Estimators computed from adaptively collected data do not behave like th...
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Orthogonal Machine Learning: Power and Limitations
Double machine learning provides √(n)consistent estimates of parameters...
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Improving Gibbs Sampler Scan Quality with DoGS
The pairwise influence matrix of Dobrushin has long been used as an anal...
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Measuring Sample Quality with Kernels
Approximate Markov chain Monte Carlo (MCMC) offers the promise of more r...
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Measuring Sample Quality with Diffusions
Standard Markov chain Monte Carlo diagnostics, like effective sample siz...
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JetImages  Deep Learning Edition
Building on the notion of a particle physics detector as a camera and th...
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Fuzzy Jets
Collimated streams of particles produced in high energy physics experime...
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Measuring Sample Quality with Stein's Method
To improve the efficiency of Monte Carlo estimation, practitioners are t...
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Corrupted Sensing: Novel Guarantees for Separating Structured Signals
We study the problem of corrupted sensing, a generalization of compresse...
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Distributed Lowrank Subspace Segmentation
Vision problems ranging from image clustering to motion segmentation to ...
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The asymptotics of ranking algorithms
We consider the predictive problem of supervised ranking, where the task...
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Combinatorial clustering and the beta negative binomial process
We develop a Bayesian nonparametric approach to a general family of late...
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Distributed Matrix Completion and Robust Factorization
If learning methods are to scale to the massive sizes of modern datasets...
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FeatureWeighted Linear Stacking
Ensemble methods, such as stacking, are designed to boost predictive acc...
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Lester Mackey
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Machine Learning Researcher at Microsoft Research New England since 2016, Assistant Professor at Stanford University from 20132016, Simons Math+X Postdoc at Stanford University from 20122013, Doctoral Student at UC Berkeley from 20072012, Research Intern at Google 2011, Team Partner at The Ensemble 2009, Recommender System Architect at Umamibud 2008, Research Intern at AT&T Labs 2007, Research Intern at Princeton University 2006, Software Design and Engineering Intern at Microsoft 2005, Research Intern at Intel 2004