From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Steinberger described the media monitoring software developed by JRC which gathers and analyses between 80,000 and 100,000 news articles per day in up to fifty languages. The software clusters related news, classifies the articles into hundreds of categories, extracts information from them, fuse...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Latent force models encode the interaction between multiple related dynamical systems in the form of a kernel or covariance function. Each variable to be modeled is represented as the output of a differential equation and each differential equation is driven by a weighted sum of latent functions ...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Identifying the features of objects becomes a challenge when those features can change in their appearance. We introduce the Transformed Indian Buffet Process (tIBP), and use it to define a nonparametric Bayesian model that infers features that can transform across instantiations. We show that t...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

We study the problem of segmenting specific white matter structures of interest from Diffusion Tensor (DT-MR) images of the human brain. This is an important requirement in many Neuroimaging studies: for instance, to evaluate whether a brain structure exhibits group level differences as a functi...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in terms of the total reward received. In each time step the agen...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Grefenstette introduced Exalead as a global software provider in the enterprise and Web search markets, which tries to integrate research functions in one piece of software, combining Internet, desktop and enterprise functionality. The firm’s technology makes it possible to integrate structured ...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

We present a novel probabilistic model for distributions over sets of structures — for example, sets of sequences, trees, or graphs. The critical characteristic of our model is a preference for diversity: sets containing dissimilar structures are more likely. Our model is a marriage of str...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Bayesian methods of matrix factorization (MF) have been actively explored recently as promising alternatives to classical singular value decomposition. In this paper, we show that, despite the fact that the optimization problem is non-convex, the global optimal solution of variational Bayesian (...

From: VideoLectures on Mon, Mar 28 2011 7:30 PM

Bayesian approaches to utility elicitation typically adopt (myopic) expected value of information (EVOI) as a natural criterion for selecting queries. However, EVOI-optimization is usually computationally prohibitive. In this paper, we examine EVOI optimization using choice queries, queries in w...

From: VideoLectures on Mon, Mar 28 2011 7:29 PM

We study worst-case bounds on the quality of any fixed point assignment of the max-product algorithm for Markov Random Fields (MRF). We start proving a bound independent of the MRF structure and parameters. Afterwards, we show how this bound can be improved for MRFs with particular structures su...

From: VideoLectures on Mon, Mar 28 2011 7:29 PM

Modern Paris was indelibly shaped by the rebuilding project ordered by Napoleon III and carried out by Baron Haussmann in the 1850s and '60s. The large-scale demolition of whole neighborhoods in central Paris, coupled with a boom in industrial development outside the city, cemented a class ...

From: VideoLectures on Mon, Mar 28 2011 7:29 PM

From: VideoLectures on Sat, Mar 26 2011 11:28 AM

Undirected graphical models encode in a graph G the dependency structure of a random vector Y. In many applications, it is of interest to model Y given another random vector X as input. We refer to the problem of estimating the graph G(x) of Y conditioned on X=x as "graph-valued regression". In ...

From: VideoLectures on Sat, Mar 26 2011 11:28 AM

Since the discovery of sophisticated fully polynomial randomized algorithms for a range of #P problems (Karzanov et al., 1991; Jerrum et al., 2001; Wilson, 2004), theoretical work on approximate inference in combinatorial spaces has focused on Markov chain Monte Carlo methods. Despite their stro...

From: VideoLectures on Sat, Mar 26 2011 11:28 AM

Despite the ubiquity of clustering as a tool in unsupervised learning, there is not yet a consensus on a formal theory, and the vast majority of work in this direction has focused on unsupervised clustering. We study a recently proposed framework for supervised clustering where there is access t...

From: VideoLectures on Sat, Mar 26 2011 11:27 AM

We propose a new supervised learning framework for visual object counting tasks, such as estimating the number of cells in a microscopic image or the number of humans in surveillance video frames. We focus on the practically-attractive case when the training images are annotated with dots (one d...

From: VideoLectures on Sat, Mar 26 2011 11:27 AM

Computing two-way and multi-way set similarities is a fundamental problem. This study focuses on estimating 3-way resemblance (Jaccard similarity) using b-bit minwise hashing. While traditional minwise hashing methods store each hashed value using 64 bits, b-bit minwise hashing only stores the l...

From: VideoLectures on Sat, Mar 26 2011 11:27 AM

In this paper we consider the fundamental problem of semi-supervised kernel function learning. We propose a general regularized framework for learning a kernel matrix, and then demonstrate an equivalence between our proposed kernel matrix learning framework and a general linear transformation le...

From: VideoLectures on Sat, Mar 26 2011 11:27 AM

We consider the problem of reinforcement learning in high-dimensional spaces when the number of features is bigger than the number of samples. In particular, we study the least-squares temporal difference (LSTD) learning algorithm when a space of low dimension is generated with a random projecti...

From: VideoLectures on Sat, Mar 26 2011 11:27 AM

The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an ...