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Found 8 papers, 0 papers with code
Distribution Learning Meets Graph Structure Sampling
This work establishes a novel link between the problem of PAC-learning high-dimensional graphical models and the task of (efficient) counting and sampling of graph structures, using an online learning framework.
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.
