We characterize the effectiveness of a classical algorithm for recovering the Markov graph of
a general discrete pairwise graphical model from iid samples. The algorithm is …

We provide time-and sample-efficient algorithms for learning and testing latent-tree Ising
models, ie Ising models that may only be observed at their leaf nodes. On the learning side …

Given an observational study with $ n $ independent but heterogeneous units, our goal is to
learn the counterfactual distribution for each unit using only one $ p $-dimensional sample …

The goal of generative machine learning is to model the probability distribution underlying a
given data set. This probability distribution helps to characterize the generation process of …

We revisit the well-studied problem of efficiently learning the underlying structure and
parameters of an Ising model from data. Current algorithmic approaches achieve essentially …

We consider the question of learning the natural parameters of a $ k $ parameter\textit
{minimal} exponential family from iid samples in a computationally and statistically efficient …

We address the problem of learning state-variable relationships across different episodes in
Partially Observable Markov Decision Processes (POMDPs) to improve planning …

We address the problem of learning relationships on state variables in Partially Observable
Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we …

We consider the problem of inferring a graphical Potts model on a population of variables.
This inverse Potts problem generally involves the inference of a large number of parameters …

The recent emergence of novel computational devices, such as quantum computers,
coherent Ising machines, and digital annealers presents new opportunities for hardware …