Seminar

Speaker: Shai Ben-David

Support Vector machines (SVM's) is one of the most useful and widely applicable machine learning techniques. Each concrete application of SVMs depends on a successful choice of a "kernel matrix". So far, most of the work in this area has focused on developing a variety of kernels and efficient algorithms for employing SVMs with these kernels. Relatively little research attention has been given to the question of how to pick a suitable kernel for any particular learning task at hand.

In this work, we analyze exactly that issue.

Speaker: Pascal Poupart

Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is mostly used for offline learning in simulated environments. We propose a new algorithm, called BEETLE, for effective online learning that is computationally efficient while minimizing the amount of exploration.

Speaker: David Pal

Stability is a common tool to verify the validity of sample based algorithms. In clustering it is widely used to tune the parameters of the algorithm, such as the number k of clusters. In spite of the popularity of stability in practical applications, there has been very little theoretical analysis of this notion. In this paper we provide a formal definition of stability and analyze some of its basic properties.

Speaker: Zhenmei Gu

Hidden Markov models (HMMs) are powerful statistical models that have found successful applications in Information Extraction (IE). In current approaches to applying HMMs to IE, an HMM is used to model text at the document level, i.e., the entire document is modeled by an HMM. This modeling might cause undesired redundancy in extraction in the sense that more than one filler is identified and extracted.

Speaker: Zhenmei Gu

Information Extraction (IE) summarizes a collection of textual documents into a structual representation by identifying specific facts from text. The naive Bayes model is one of the first statistical models that have been applied to IE for learning extraction patterns from labelled data. In spite of the simplicity and the popularity of the naive Bayes model, we have observed a formulation problem in previous work on naive Bayes IE. In this talk, we present a formal naive Bayes modelling for IE, by which the induced formula for the filler probability estimation is more theoretically sound.

Speaker: Thomas Zeugmann, Hokkaido University, Japan

Clustering algorithms working with a matrix of pairwise similarities (kernel matrix) for the data are widely known and used, a particularly popular class being spectral clustering algorithms. In contrast, algorithms working with the pairwise distance matrix have been studied rarely for clustering. This is surprising, as in many applications, distances are directly given, and computing similarities involves another step that is error-prone, since the kernel has to be chosen appropriately, albeit computationally cheap.

Speaker: Hojjat Ghaderi, University of Toronto

The coordination of cooperating but autonomous agents is a core problem in multiagent systems research. A team of agents is said to have joint ability to achieve a goal if despite any incomplete knowledge or even false beliefs that they may have about the world or each others, they still know enough to get to a goal state, should they choose to do so.

Speaker: Milen Pavlov

Research collaborations are always encouraged, as they often yield good results. However, researcher networks contain massive amounts of experts in various disciplines and it is difficult for the individual researcher to decide which experts will match his own expertise best.

Speaker: Dana Wilkinson

There are a variety of domains where it is desirable to learn a representation of an environment defined by a stream of sensori-motor experience. This talk introduces and formalizes Subjective Mapping, a novel approach to this problem.

Speaker: Yves Lesperance (York University)

In this talk, I will present a formal model of plan recognition for inclusion in a cognitive agent programming framework.