Add to ORKGWe address problems with a recent attempt by Eva, Hartmann and Rad to justify the Kullback-Leibler divergence minimization solution to van Fraassen's Judy Benjamin problem
Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving...
In this paper, we provide a novel derivation of the probability hypothesis density (PHD) filter with...
Kullback-Leibler (KL) divergence is widely used for variational inference of Bayesian Neural Network...
We address problems with a recent attempt by Eva, Hartmann and Rad to justify the Kullback-Leibler d...
The problem of estimating the Kullback-Leibler divergence D(P||Q) between two unknown distributions ...
Van Fraassen's Judy Benjamin problem has generally been taken to show that not all rational changes ...
The goal of this short note is to discuss the relation between Kullback--Leibler divergence and tota...
The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies...
The Jensen-Shannon divergence is a renown bounded symmetrization of the unbounded Kullback-Leibler d...
We provide optimal lower and upper bounds for the augmented Kullback-Leibler divergence in terms of ...
Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving...
Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics....
The Kullback-Leibler (KL) divergence is one of the most fundamental metrics in information theory an...
International audienceWe apply divergences to project a prior guess discrete probability law on pq e...
To ensure stability of learning, state-of-the-art generalized policy iteration algorithms augment th...
Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving...
In this paper, we provide a novel derivation of the probability hypothesis density (PHD) filter with...
Kullback-Leibler (KL) divergence is widely used for variational inference of Bayesian Neural Network...
We address problems with a recent attempt by Eva, Hartmann and Rad to justify the Kullback-Leibler d...
The problem of estimating the Kullback-Leibler divergence D(P||Q) between two unknown distributions ...
Van Fraassen's Judy Benjamin problem has generally been taken to show that not all rational changes ...
The goal of this short note is to discuss the relation between Kullback--Leibler divergence and tota...
The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies...
The Jensen-Shannon divergence is a renown bounded symmetrization of the unbounded Kullback-Leibler d...
We provide optimal lower and upper bounds for the augmented Kullback-Leibler divergence in terms of ...
Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving...
Kullback-Leibler divergence and the Neyman-Pearson lemma are two fundamental concepts in statistics....
The Kullback-Leibler (KL) divergence is one of the most fundamental metrics in information theory an...
International audienceWe apply divergences to project a prior guess discrete probability law on pq e...
To ensure stability of learning, state-of-the-art generalized policy iteration algorithms augment th...
Van Fraassen's Judy Benjamin problem asks how one ought to update one's credence in A upon receiving...
In this paper, we provide a novel derivation of the probability hypothesis density (PHD) filter with...
Kullback-Leibler (KL) divergence is widely used for variational inference of Bayesian Neural Network...