no code implementations • 13 Nov 2023 • Bijan Mazaheri, Spencer Gordon, Yuval Rabani, Leonard Schulman
The task of learning these structures from data is known as ``causal discovery''.
no code implementations • 25 Sep 2023 • Spencer L. Gordon, Erik Jahn, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman
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.
no code implementations • 22 Dec 2021 • Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman
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.
no code implementations • 29 Dec 2020 • Spencer L. Gordon, Bijan Mazaheri, Yuval Rabani, Leonard J. Schulman
We give an algorithm for source identification of a mixture of $k$ product distributions on $n$ bits.
no code implementations • 16 Jul 2020 • Spencer Gordon, Bijan Mazaheri, Leonard J. Schulman, Yuval Rabani
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.
no code implementations • 15 Apr 2020 • Sébastien Bubeck, Yuval Rabani, Mark Sellke
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.
no code implementations • 10 Apr 2015 • Jian Li, Yuval Rabani, Leonard J. Schulman, Chaitanya Swamy
We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets.
no code implementations • 7 Dec 2012 • Yuval Rabani, Leonard Schulman, Chaitanya Swamy
(In the topic-model learning setting, the mixture constituents correspond to the topic distributions.)