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Found 7 papers, 0 papers with code
Total Variation Distance Estimation Is as Easy as Probabilistic Inference
In particular, we present an efficient, structure-preserving reduction from relative approximation of TV distance to probabilistic inference over directed graphical models.
Efficient inference of interventional distributions
For sets $\mathbf{X},\mathbf{Y}\subseteq \mathbf{V}$, and setting ${\bf x}$ to $\mathbf{X}$, let $P_{\bf x}(\mathbf{Y})$ denote the interventional distribution on $\mathbf{Y}$ with respect to an intervention ${\bf x}$ to variables ${\bf x}$.
Testing Product Distributions: A Closer Look
We study the problems of identity and closeness testing of $n$-dimensional product distributions.
Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu
For a distribution $P$ on $\Sigma^n$ and a tree $T$ on $n$ nodes, we say $T$ is an $\varepsilon$-approximate tree for $P$ if there is a $T$-structured distribution $Q$ such that $D(P\;||\;Q)$ is at most $\varepsilon$ more than the best possible tree-structured distribution for $P$.
Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
We design efficient distance approximation algorithms for several classes of structured high-dimensional distributions.
Learning and Sampling of Atomic Interventions from Observations
Assuming that $G$ has bounded in-degree, bounded c-components ($k$), and that the observational distribution is identifiable and satisfies certain strong positivity condition, we give an algorithm that takes $m=\tilde{O}(n\epsilon^{-2})$ samples from $P$ and $O(mn)$ time, and outputs with high probability a description of a distribution $\hat{P}$ such that $d_{\mathrm{TV}}(P_x, \hat{P}) \leq \epsilon$, and: 1.
Interpretable Classification via Supervised Variational Autoencoders and Differentiable Decision Trees
As deep learning-based classifiers are increasingly adopted in real-world applications, the importance of understanding how a particular label is chosen grows.
