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07/17/2023
Reflections from the Workshop on AI-Assisted Decision Making for Conservation
In this white paper, we synthesize key points made during presentations ...
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06/20/2023
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Randomized experiments often need to be stopped prematurely due to the t...
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05/29/2023
Do Language Models Know When They're Hallucinating References?
Current state-of-the-art language models (LMs) are notorious for generat...
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05/24/2023
Learning Rate Free Bayesian Inference in Constrained Domains
We introduce a suite of new particle-based algorithms for sampling on co...
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01/14/2023
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Kernel two-sample testing provides a powerful framework for distinguishi...
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11/17/2022
A Finite-Particle Convergence Rate for Stein Variational Gradient Descent
We provide a first finite-particle convergence rate for Stein variationa...
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11/10/2022
Controlling Moments with Kernel Stein Discrepancies
Quantifying the deviation of a probability distribution is challenging w...
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10/24/2022
Budget-Constrained Bounds for Mini-Batch Estimation of Optimal Transport
Optimal Transport (OT) is a fundamental tool for comparing probability d...
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09/26/2022
Targeted Separation and Convergence with Kernel Discrepancies
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD...
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09/21/2022
Adaptive Bias Correction for Improved Subseasonal Forecasting
Subseasonal forecasting x2013 predicting temperature and precipitation 2...
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08/21/2022
Efficient Concentration with Gaussian Approximation
Concentration inequalities for the sample mean, like those due to Bernst...
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04/04/2022
Scalable Spike-and-Slab
Spike-and-slab priors are commonly used for Bayesian variable selection,...
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02/19/2022
Gradient Estimation with Discrete Stein Operators
Gradient estimation – approximating the gradient of an expectation with ...
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12/06/2021
Bounding Wasserstein distance with couplings
Markov chain Monte Carlo (MCMC) provides asymptotically consistent estim...
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11/15/2021
Distribution Compression in Near-linear Time
In distribution compression, one aims to accurately summarize a probabil...
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10/04/2021
Generalized Kernel Thinning
The kernel thinning (KT) algorithm of Dwivedi and Mackey (2021) compress...
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09/21/2021
Learned Benchmarks for Subseasonal Forecasting
We develop a subseasonal forecasting toolkit of simple learned benchmark...
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08/25/2021
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
Many modern learning algorithms mitigate bias by enforcing fairness acro...
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07/05/2021
Near-optimal inference in adaptive linear regression
When data is collected in an adaptive manner, even simple methods like o...
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06/23/2021
Sampling with Mirrored Stein Operators
We introduce a new family of particle evolution samplers suitable for co...
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05/12/2021
Kernel Thinning
We introduce kernel thinning, a new procedure for compressing a distribu...
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05/07/2021
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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05/03/2021
Initialization and Regularization of Factorized Neural Layers
Factorized layers–operations parameterized by products of two or more ma...
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04/20/2021
Knowledge Distillation as Semiparametric Inference
A popular approach to model compression is to train an inexpensive stude...
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10/20/2020
Model-specific Data Subsampling with Influence Functions
Model selection requires repeatedly evaluating models on a given dataset...
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09/22/2020
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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07/24/2020
Cross-validation Confidence Intervals for Test Error
This work develops central limit theorems for cross-validation and consi...
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07/06/2020
Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and non-convergence in app...
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06/16/2020
Metrizing Weak Convergence with Maximum Mean Discrepancies
Theorem 12 of Simon-Gabriel Schölkopf (JMLR, 2018) seemed to close a...
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06/12/2020
Minimax Estimation of Conditional Moment Models
We develop an approach for estimating models described via conditional m...
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05/08/2020
Optimal Thinning of MCMC Output
The use of heuristics to assess the convergence and compress the output ...
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03/20/2020
Weighted Meta-Learning
Meta-learning leverages related source tasks to learn an initialization ...
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03/02/2020
Approximate Cross-validation: Guarantees for Model Assessment and Selection
Cross-validation (CV) is a popular approach for assessing and selecting ...
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11/04/2019
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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08/06/2019
Debiasing Linear Prediction
Standard methods in supervised learning separate training and prediction...
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07/01/2019
A Kernel Stein Test for Comparing Latent Variable Models
We propose a nonparametric, kernel-based test to assess the relative goo...
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06/19/2019
Minimum Stein Discrepancy Estimators
When maximum likelihood estimation is infeasible, one often turns to sco...
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06/19/2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond
Sampling with Markov chain Monte Carlo methods typically amounts to disc...
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05/09/2019
Stein Point Markov Chain Monte Carlo
An important task in machine learning and statistics is the approximatio...
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12/05/2018
Model Compression with Generative Adversarial Networks
More accurate machine learning models often demand more computation and ...
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10/29/2018
Global Non-convex Optimization with Discretized Diffusions
An Euler discretization of the Langevin diffusion is known to converge t...
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09/19/2018
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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06/20/2018
Random Feature Stein Discrepancies
Computable Stein discrepancies have been deployed for a variety of appli...
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05/31/2018
DeepMiner: Discovering Interpretable Representations for Mammogram Classification and Explanation
We propose DeepMiner, a framework to discover interpretable representati...
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03/27/2018
Stein Points
An important task in computational statistics and machine learning is to...
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03/13/2018
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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12/18/2017
Accurate Inference for Adaptive Linear Models
Estimators computed from adaptively collected data do not behave like th...
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11/01/2017
Orthogonal Machine Learning: Power and Limitations
Double machine learning provides √(n)-consistent estimates of parameters...
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07/18/2017
Improving Gibbs Sampler Scan Quality with DoGS
The pairwise influence matrix of Dobrushin has long been used as an anal...
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03/06/2017