Invited speakers

Prof. Bin Yu (UC Berkeley, USA)

Stability and Large Scale Statistical Inference    (Slides)

Abstract
Information technolgoy has enabled collection of large amounts of and high-dimensional data across fields in science, engineering, social science, commerce, and beyond. Reproducibility is imperative for any meaningful discovery from these data. Statistical machine learning analyses are often used to bring the discoveries and, as a minimal manifestitation of reproducibility, conclusions from statistical analyses have to be stable or robust to "reasonable" perturbations to data and to the model used. Examples of data perturbation schemes include Jacknife, bootstrap, and cross-validation while robust statistics aims at studying perturbations to models. In this talk, the indispensable requirement of stability is advocated for large scale statistical inference. A movie-reconstruction from fMRI data motivates the need for stablity, and a new estimation stability revision on CV for Lasso is proposed to lead to a much simpler and more reliable model. Finally, a novel stability analytical argument is seen to drive new results that shed light on the intriguing interactions between sample to sample varibility and heavier tail error distribution (e.g. double-exponential) in high-dim robust statistics.

Bio
Bin Yu is Chancellor's Professor in the Departments of Statistics and of Electrical Engineering & Computer Science at UC Berkeley. She has published over 100 scientific papers in premier journals in Statistics, EECS, remote sensing and neuroscience, in a wide range of research areas including empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference (boosting and Lasso and sparse modeling in general), and interdisciplinary data problems.  She has served on many editorial boards for journals such as Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research.

She was a 2006 Guggenheim Fellow, co-recipient of the Best Paper Award of IEEE Signal Processing Society in 2006, and the 2012 Tukey Memorial Lecturer of the Bernoulli Society (selected every four years). She is a Fellow of AAAS, IEEE, IMS (Institute of Mathematical Statistics) and ASA (American Statistical Association).

She is currently President-Elect of IMS (Institute of Mathematical Statistics). She is serving on the Scientific Advisory Board of IPAM (Institute ofr Pure and Applied Mathematics) and on the Board of Mathematical Sciences and Applications of NAS. She was co-chair of the National Scientific Committee of SAMSI (Statistical and Applied Mathematical Sciences Institute), and on the Board of Governors of IEEE-IT Society.

Prof. Marcello Pelillo (Università Ca' Foscari Venezia, Italy)

(Marcello has to cancel the trip due to unexpected family reasons.)

Similarity-Based Pattern Recognition: A Game-Theoretic Perspective

Abstract [Read the full extended abstract]

The classical approach to deal with non-geometric (dis)similarities is "em- bedding", which refers to any procedure that takes a set of (dis)similarities as input and produces a vectorial representation of the data as output, such that the proximities are either locally or globally preserved. This is an (approximate or ideal) isometric mapping which finds a set of vectors in an instance-specific Euclidean space that are capable of describing the data satisfactorily. Embedding approaches are all based on the assumption that the non-geometricity of similarity information can be eliminated or some-how approximated away. When this is not the case, i.e., when there is significant information content in the non-geometricity of the data, however, alternative approaches are needed.

In this talk, I will maintain that game theory offers an elegant and powerful conceptual framework which serves well this purpose. The development of game theory in the early 1940's by von Neumann was a reaction against the then dominant view that problems in economic theory can be formulated using standard methods from optimization theory. Indeed, most real-world economic problems typically involve conflicting interactions among decision-making agents that cannot be adequately captured by a single (global) objective function, thereby requiring a different, more sophisticated treatment. Accordingly, the main point made by game theorists is to shift the emphasis from optimality criteria to equilibrium conditions, namely to the search of a balance among multiple interacting forces. Interestingly, the development of evolutionary game theory in the late 1970's by Maynard Smith offered a dynamical systems perspective to game theory, an element which was totally missing in the traditional formulation, and provided powerful tools to deal with the equilibrium selection problem.

As it provides an abstract theoretically-founded framework to elegantly model complex scenarios, game theory has found a variety of applications not only in economics and, more generally, in the social sciences but also in different fields of engineering and information technologies. In the talk, I will describe recent attempts aimed at formulating (or interpreting) several pattern recognition and machine learning problems from a game-theoretic perspective. Indeed, many problems within these fields can naturally be formulated at an abstract level in terms of a game where (pure) strategies correspond to class labels and the payoff function is expressed in terms of competition between the hypotheses of class membership. In particular, I will focus on data clustering, semi-supervised learning, structural matching, and contextual pattern recognition. Further, I will discuss potential applications of game theory within the context of multiple classifier systems.

Bio

Marcello Pelillo joined the faculty of the University of Bari, Italy, as an Assistant professor of computer science in 1991. Since 1995, he has been with the University of Venice, Italy, where he is currently a Full Professor of Computer Science.

He leads the Computer Vision and Pattern Recognition Group and has served from 2004 to 2010 as the Chair of the board of studies of the Computer Science School. He held visiting research positions at Yale University, the University College London, McGill University, the University of Vienna, York University (UK), and the National ICT Australia (NICTA). He has published more than 130 technical papers in refereed journals, handbooks, and conference proceedings in the areas of computer vision, pattern recognition and neural computation.

He serves (or has served) on the editorial board for the journals IEEE Transactions on Pattern Analysis and Machine Intelligence and Pattern Recognition, and is regularly on the program committees of the major international conferences and workshops of his fields. In 1997, he co-established a new series of international conferences devoted to energy minimization methods in computer vision and pattern recognition (EMMCVPR) which has now reached the ninth edition. He is (or has been) scientific coordinator of several research projects, including SIMBAD, an EU-FP7 project devoted to similarity-based pattern analysis and recognition. Prof. Pelillo is a Fellow of the IEEE and of the IAPR.

Prof. Xin Yao (University of Birmingham, U.K.)

From Evolutionary Computation to Ensemble Learning    (Slides)

Abstract

Designing a monolithic system for a large and complex learning task is hard. Divide-and-conquer is a common strategy in tackling such large and complex problems. Artificial speciation and niching have long been used in evolutionary computation as one way towards automatic diivide-and-conquer. It turns out that several key ideas in speciated evolutionary algorithms are closely linked to ensemble learning in general, especially diversity in ensembles. This talk reviews selected work on this topic and illustrates the link between evolutionary computation and ensemble learning using the example of negative correlation learning. Then the importance of diversity is demonstrated using examples from online learning and class imbalance learning. As a practical solution towards accurate and diverse ensembles, multi-objective ensemble learning is advocated. Finally, a recent application of multi-objective learning to software effort estimation will be described.

Xin Yao is a professor of computer science from the University of Birmingham, UK. He was also a Distinguished Visiting Professor of the University of Science and Technology of China (USTC), P. R. China, and a visiting professor of three other universities. He is a Fellow of the IEEE, Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation, an associate editor or an editorial board member of 11 other journals. He was the winner of 2001 IEEE Donald G. Fink prize paper award and several other best paper awards. His research interests include evolutionary computation, neural network ensembles, and their applications. He has more than 300 refereed publications in those areas. He is currently the Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) , which is focused on applied research and knowledge transfer to industry.

References:
• X. Yao and Y. Liu, ``Making use of population information in evolutionary artificial neural networks,'' IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 28(3):417-425, June 1998.
• A Chandra and X. Yao, ``Ensemble learning using multi-objective evolutionary algorithms,'' Journal of Mathematical Modelling and Algorithms, 5(4):417-445, December 2006.
• L. L. Minku and X. Yao, "DDD: A New Ensemble Approach For Dealing With Concept Drift,'' IEEE Transactions on Knowledge and Data Engineering, 24(4):619-633, April 2012.
• S. Wang and X. Yao, ``Multi-Class Imbalance Problems: Analysis and Potential Solutions,'' IEEE Transactions on Systems, Man and Cybernetics, Part B, 42(4):1119-1130, August 2012.
• L. L. Minku and X. Yao, ``Software Effort Estimation as a Multi-objective Learning Problem,'' ACM Transactions on Software Engineering and Methodology, to appear.

Bio

Xin Yao is a professor of computer science from the University of Birmingham, UK. He was also a Distinguished Visiting Professor of the University of Science and Technology of China (USTC), P. R. China, and a visiting professor of three other universities. He is a Fellow of the IEEE, Editor-in-Chief (2003-08) of IEEE Transactions on Evolutionary Computation, an associate editor or an editorial board member of 11 other journals. He was the winner of 2001 IEEE Donald G. Fink prize paper award and several other best paper awards. His research interests include evolutionary computation, neural network ensembles, and their applications. He has more than 300 refereed publications in those areas. He is currently the Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) , which is focused on applied research and knowledge transfer to industry.

Prof. Marco Loog (Delft University of Technology, The Netherlands; University of Copenhagen, Denmark)

Semi-Supervised Learning: Dipping, Loss, Constraints, and the General Interest in Basic Research Questions    (Slides)

Abstract

I will provide a coarse overview of the principal approaches to semi-supervised learning and pass in review my own work in that same field. The latter runs counter to the mainstream and provides a rather different take on semi-supervised learning. Next to introducing the basic tenet in my reserach, I will roughly sketch the main approach I have been studying, provide some simple instantiations of it, and discuss the main challenges ahead.

Bio

Marco Loog received an M.Sc. degree in mathematics from Utrecht University and in 2004 a Ph.D. degree from the Image Sciences Institute for the development and improvement of contextual statistical pattern recognition methods and their use in the processing and analysis of images. Since 2008, Marco resides at Delft University of Technology where he works as an assistant professor in the Pattern Recognition Laboratory. He currently is chair of TC1 of the IAPR, holds a bunch of associate editorships, is honorary full professor in pattern recognition at the University of Copenhagen, and is also affiliated to Eindhoven University of Technology. Marco's research interests include multiscale image analysis, semi-supervised and multiple instance learning, saliency, computational perception, the dissimilarity approach, and black math.