We give an algorithm for source identification of a mixture of k product distributions on n bits.
This is a fundamental problem in machine learning with many applications. Our algorithm …

We introduce topological differential testing (TDT), an approach to extracting the consensus
behavior of a set of programs on a corpus of inputs. TDT uses the topological notion of a …

Feature embedding is an emerging research area which intends to transform features from
the original space into a new space to support effective learning. Many feature embedding …

Motivated by the prevailing paradigm of using unsupervised learning for efficient exploration
in reinforcement learning (RL) problems [tang2017exploration, bellemare2016unifying], we …

The clustering task consists in delivering labels to the members of a sample. For most data
sets, some individuals are ambiguous and intrinsically difficult to attribute to one or another …

The problem of identifiability of finite mixtures of finite product measures is studied. A mixture
model with $ K $ mixture components and $ L $ observed variables is considered, where …

When cancer develops, gene mutations do not occur independently, prompting researchers
to pose scientific hypotheses about their interactions. Synthetic lethal interactions, which …

We formulate the problem of fake news detection using distributed inexpert agents. We
consider the source for news/statements as a binary source (to model true vs. false …

The clustering task consists in partitioning elements of a sample into homogeneous groups.
Most datasets contain individuals that are ambiguous and intrinsically difficult to attribute to …

The reliability of ML methods is critical in applications that involve decision making. The goal
of this thesis is to propose new methods for risk control in several learning tasks: novelty …