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Discriminant Analysis under f-Divergence Measures [PDF]
Entropy, 2022 In statistical inference, the information-theoretic performance limits can often be expressed in terms of a statistical divergence between the underlying statistical models (e.g., in binary hypothesis testing, the error probability is related to the ...
Anmol Dwivedi, Sihui Wang, Ali Tajer
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IEEE Transactions on Information Theory, 2016 This paper develops systematic approaches to obtain $f$-divergence inequalities, dealing with pairs of probability measures defined on arbitrary alphabets.
Sason, Igal, Verdú, Sergio
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Reconstructing Dynamic Gene Regulatory Networks Using f-Divergence from Time-Series scRNA-Seq Data [PDF]
Current Issues in Molecular BiologyInferring time-varying gene regulatory networks from time-series single-cell RNA sequencing (scRNA-seq) data remains a challenging task. The existing methods have notable limitations as most are either designed for reconstructing time-varying networks ...
Yunge Wang +5 more
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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data [PDF]
StatsImputing missing values in high-dimensional time-series data remains a significant challenge in statistics and machine learning. Although various methods have been proposed in recent years, many struggle with limitations and reduced accuracy ...
Wen-Shan Liu +4 more
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$f$-Divergence Inequalities via Functional Domination [PDF]
2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 2016 This paper considers derivation of $f$-divergence inequalities via the approach of functional domination. Bounds on an $f$-divergence based on one or several other $f$-divergences are introduced, dealing with pairs of probability measures defined on ...
Sason, Igal, Verdú, Sergio
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Non-commutative f-divergence functional [PDF]
Mathematische Nachrichten, 2013 We introduce the non-commutative $f$-divergence functional $\Theta(\widetilde{A},\widetilde{B}):=\int_TB_t^{\frac{1}{2}}f\left(B_t^{-\frac{1}{2}} A_tB_t^{-\frac{1}{2}}\right)B_t^{\frac{1}{2}}d\mu(t)$ for an operator convex function $f$, where $\widetilde{
Kian, Mohsen, Moslehian, Mohammad Sal
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Ensemble estimation of multivariate f-divergence [PDF]
2014 IEEE International Symposium on Information Theory, 2014 f-divergence estimation is an important problem in the fields of information theory, machine learning, and statistics. While several divergence estimators exist, relatively few of their convergence rates are known.
Hero III, Alfred O., Moon, Kevin R.
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A new quantum version of f-divergence [PDF]
, 2018 This paper proposes and studies new quantum version of $f$-divergences, a class of convex functionals of a pair of probability distributions including Kullback-Leibler divergence, Rnyi-type relative entropy and so on.
A. Ebadian +7 more
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f-Divergence constrained policy improvement [PDF]
, 2018 To ensure stability of learning, state-of-the-art generalized policy iteration algorithms augment the policy improvement step with a trust region constraint bounding the information loss.
Belousov, Boris, Peters, Jan
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On surrogate loss functions and $f$-divergences
The Annals of Statistics, 2008 The goal of binary classification is to estimate a discriminant function $\gamma$ from observations of covariate vectors and corresponding binary labels.
Jordan, Michael I. +2 more
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