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Found 8 papers, 0 papers with code
Discrete Nonparametric Causal Discovery Under Latent Class Confounding
The task of learning these structures from data is known as ``causal discovery''.
Identification of Mixtures of Discrete Product Distributions in Near-Optimal Sample and Time Complexity
We consider the problem of identifying, from statistics, a distribution of discrete random variables $X_1,\ldots, X_n$ that is a mixture of $k$ product distributions.
Causal Inference Despite Limited Global Confounding via Mixture Models
A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the random variables that is Markovian on the graph.
Source Identification for Mixtures of Product Distributions
We give an algorithm for source identification of a mixture of $k$ product distributions on $n$ bits.
The Sparse Hausdorff Moment Problem, with Application to Topic Models
We give an algorithm for identifying a $k$-mixture using samples of $m=2k$ iid binary random variables using a sample of size $\left(1/w_{\min}\right)^2 \cdot\left(1/\zeta\right)^{O(k)}$ and post-sampling runtime of only $O(k^{2+o(1)})$ arithmetic operations.
Online Multiserver Convex Chasing and Optimization
We introduce the problem of $k$-chasing of convex functions, a simultaneous generalization of both the famous k-server problem in $R^d$, and of the problem of chasing convex bodies and functions.
Learning Arbitrary Statistical Mixtures of Discrete Distributions
We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets.
Learning Mixtures of Arbitrary Distributions over Large Discrete Domains
(In the topic-model learning setting, the mixture constituents correspond to the topic distributions.)
