Practitioners of data mining and machine learning have long observed that the imbalance of
classes in a data set negatively impacts the quality of classifiers trained on that data …

We introduce a new approach for designing computationally efficient learning algorithms
that are tolerant to noise, and we demonstrate its effectiveness by designing algorithms with …

We consider a new formulation of abduction in which degrees of" plausibility" of
explanations, along with the rules of the domain, are learned from concrete examples …

Machine learning and statistics typically focus on building models that capture the vast
majority of the data, possibly ignoring a small subset of data as" noise" or" outliers." By …

Linear classifiers are a basic model in supervised learning. We study the problem of
learning a mixture of linear classifiers over Gaussian marginals. Despite significant interest …

We formulate a new variant of the problem of planning in an unknown environment, for
which we can provide algorithms with reasonable theoretical guarantees in spite of large …

We revisit the problem of online learning with sleeping experts/bandits: in each time step,
only a subset of the actions are available for the algorithm to choose from (and learn about) …

Work in machine learning and statistics commonly focuses on building models that capture
the vast majority of data, possibly ignoring a segment of the population as outliers. However …

Inference from an observed or hypothesized condition to a plausible cause or explanation
for this condition is known as abduction. For many tasks, the acquisition of the necessary …

We consider the problem of approximating and learning disjunctions (or equivalently,
conjunctions) on symmetric distributions over {0, 1} n. Symmetric distributions are …