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Plugin estimation of smooth optimal transport maps
Plugin estimation of smooth optimal transport maps Page 1 The Annals of Statistics 2024, Vol.
52, No. 3, 966–998 https://doi.org/10.1214/24-AOS2379 © Institute of Mathematical Statistics …
52, No. 3, 966–998 https://doi.org/10.1214/24-AOS2379 © Institute of Mathematical Statistics …
The information bottleneck problem and its applications in machine learning
Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now
playing a pivotal role in various aspect of society. The goal in statistical learning is to use …
playing a pivotal role in various aspect of society. The goal in statistical learning is to use …
Information theory with kernel methods
We consider the analysis of probability distributions through their associated covariance
operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy …
operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy …
Sliced mutual information: A scalable measure of statistical dependence
Mutual information (MI) is a fundamental measure of statistical dependence, with a myriad of
applications to information theory, statistics, and machine learning. While it possesses many …
applications to information theory, statistics, and machine learning. While it possesses many …
Geometric lower bounds for distributed parameter estimation under communication constraints
We consider parameter estimation in distributed networks, where each sensor in the network
observes an independent sample from an underlying distribution and has $ k $ bits to …
observes an independent sample from an underlying distribution and has $ k $ bits to …
Neural estimation of statistical divergences
Statistical divergences (SDs), which quantify the dissimilarity between probability
distributions, are a basic constituent of statistical inference and machine learning. A modern …
distributions, are a basic constituent of statistical inference and machine learning. A modern …
The boltzmann policy distribution: Accounting for systematic suboptimality in human models
Models of human behavior for prediction and collaboration tend to fall into two categories:
ones that learn from large amounts of data via imitation learning, and ones that assume …
ones that learn from large amounts of data via imitation learning, and ones that assume …
Convergence of smoothed empirical measures with applications to entropy estimation
This paper studies convergence of empirical measures smoothed by a Gaussian kernel.
Specifically, consider approximating P* N σ, for N σ=△ N (0, σ 2 I d), by P̑ n* N σ under …
Specifically, consider approximating P* N σ, for N σ=△ N (0, σ 2 I d), by P̑ n* N σ under …
[HTML][HTML] Empirical estimation of information measures: A literature guide
We give a brief survey of the literature on the empirical estimation of entropy, differential
entropy, relative entropy, mutual information and related information measures. While those …
entropy, relative entropy, mutual information and related information measures. While those …
Estimation based on nearest neighbor matching: from density ratio to average treatment effect
Nearest neighbor (NN) matching is widely used in observational studies for causal effects.
Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their …
Abadie and Imbens (2006) provided the first large‐sample analysis of NN matching. Their …