Source: Nicolai Schäfer (Creative Commons) This workshop is joint to ICML 2012, Edinburgh, Scotland (see the ICML 2012 website for more information). It will be held on Saturday, June 30, 2012. New deadline for contributions: May 16, 2012. Topic
This workshop concerns analysis and prediction of complex data such as objects, functions and structures. It aims to discuss various ways to extend machine learning and statistical inference to these data and especially to complex outputs prediction. A special attention will be paid to operator-valued kernels and tools for prediction in infinite dimensional space. Context and motivation
Complex data occur in
many fields such as bioinformatics, information retrieval, speech recognition,
image reconstruction, econometrics, biomedical engineering. In this workshop,
we will consider two kinds of data: functional data and object or structured data. Functional data refers to data
collected under the form of sampled curves or surfaces (longitudinal
studies, time series, images). Analysis of these data as samples of random
functions rather that a collection of individual observations is called
Functional Data Analysis (FDA). FDA involves statistics in infinite-dimensional
spaces and is closely associated to operatorial statistics. Its main approaches
include functional principal component analysis and functional regression.
Many theoretical challenges remain open in FDA and attract an increasing number of researchers. Besides functional data, object and
structure data exhibit an explicit structure like trees, graphs or sequences. For instance, documents,
molecules, social networks and again images can be easily encoded as objet
structured data. For the two last decades, both machine learning and statistics
communities have developed various approaches to take into account the
structure of the data. FDA is currently being extended to Object Data Analysis which deals with samples of object
data instead of curves while in machine learning, graphical probabilistic models
as well as kernel methods have been proposed among other methods to represent
and analyze such data. However, most of the efforts have been concentrated so far on dealing with complex
inputs. In this workshop, we would like to emphasize the problem of complex outputs
prediction which is involved for instance in multi-task learning, structured
classification and regression, and network inference. All these tasks share a common feature: they can be viewed as approximation of
vector-valued functions instead of scalar-valued functions and in the most general case, the
output space is an Hilbert space. A promising direction first developed in (Micchelli and Pontil, 2005) consists in working with Reproducing Kernel Hilbert Spaces with operator-valued kernels in order to get an appropriate framework for regularization. There is thus a strong link between recent works in machine learning about prediction of multiple or complex outputs and functional and operatorial statistics. This workshop aims at bringing together researchers from both
communities to 1) provide an overview of existing concepts and methods, 2) identify theoretical challenges in and (3) discuss practical applications and new tasks. To achieve this goal, we intend to build up from the successful
workshops organized in the machine community about structured prediction like:
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