An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

As retrieval systems become more complex, learning to rank approaches are being
developed to automatically tune their parameters. Using online learning to rank, retrieval …

Learning to rank is an increasingly important scientific field that comprises the use of
machine learning for the ranking task. New learning to rank methods are generally …

Characterizing what types of exoskeleton gaits are comfortable for users, and understanding
the science of walking more generally, require recovering a user's utility landscape …

A fundamental problem in reinforcement learning is balancing exploration and exploitation.
We address this problem in the context of model-based reinforcement learning in large …

As retrieval systems become more complex, learning to rank approaches are being
developed to automatically tune their parameters. Using online learning to rank approaches …

Bayesian approaches to preference learning using Gaussian Processes (GPs) are attractive
due to their ability to explicitly model uncertainty in users' latent utility functions; unfortunately …

Pattern mining provides useful tools for exploratory data analysis. Numerous efficient
algorithms exist that are able to discover various types of patterns in large datasets …

Web Content Extraction is an important problem that has been studied through different
approaches and algorithms. It is interested in extracting meaningful and useful data from the …

1. Introduction pany's intranet or a library catalogue, collecting the large amounts of training
data required for supervised learning is usually not feasible (Sanderson, 2010). Even in …