Assistive Machine Learning for People with Disabilities

Nowadays, there are massive amounts of heterogeneous electronic information available on the Web. People with disabilities, among other groups potentially influenced by the digital gap, face great barriers when trying to access information. Sometimes their disability makes their interaction the ICT environment (eg., computers, mobile phones, multimedia players and other hardware devices) more difficult. Furthermore, the contents are delivered in such formats that cannot be accessed by people with disability and the elderly. The challenge for their complete integration in information society has to be analyzed from different technology approaches.

Recent developments in Machine Learning are improving the way people with disabilities access to digital information resources. From the hardware perspective, Machine Learning can be a core part for the correct design of accessible interaction systems of such users with computers (such as BCI). From the contents perspective, Machine Learning can provide tools to adapt contents (for instance changing the modality in which it is accessed) to users with special needs. From the users' perspective, Machine Learning can help constructing a good user modeling, as well as the particular context in which the information is accessed.

Causality and Time Series Analysis

This symposium addresses a topic that has spurred vigorous scientific debate of late in the fields of neuroscience and machine learning: causality in time-series data. In neuroscience, causal inference in brain signal activity (EEG, MEG, fMRI, etc.) is challenged by relatively rough prior knowledge of brain connectivity and by sensor limitations (mixing of sources).

On the machine learning side, as the Causality workshop last year’s NIPS conference has evidenced for static (non-time series) data, there are issues of whether or not graphical models (directed acyclic graphs) pioneered by Judea Pearl, Peter Spirtes, and others can reliably provide a cornerstone of causal inference, whereas in neuroscience there are issues of whether Granger type causality inference is appropriate given the source mixing problem, traditionally addressed by ICA methods.

Further topics, yet to be fully explored, are non-linearity, non-Gaussianity and full causal graph inference in high-dimensional time series data. Many ideas in causality research have been developed by and are of direct interest and relevance to researchers from fields beyond ML and neuroscience: economics (i.e. the Nobel Prize winning work of the late Clive Granger, which we will pay tribute to), process and controls engineering, sociology, etc.

Despite the long-standing challenges of time-series causality, both theoretical and computational, the recent emergence of cornerstone developments and efficient computational learning methods all point to the likely growth of activity in this seminal topic.

Along with the stimulating discussion of recent research on time- series causality, we will present and highlight time-series datasets added to the Causality Workbench, which have grown out of last year’s Causality challenge and NIPS workshop, some of which are neuroscience related.

Machine Learning in Computational Biology

The field of computational biology has seen dramatic growth over the past fewyears, both in terms of new available data, new scientific questions, and newchallenges for learning and inference. In particular, biological data are oftenrelationally structured and highly diverse, well-suited to approaches thatcombine multiple weak evidence from heterogeneous sources.

These data mayinclude sequenced genomes of a variety of organisms, gene expression data frommultiple technologies, protein expression data, protein sequence and 3Dstructural data, protein interactions, gene ontology and pathway databases,genetic variation data (such as SNPs), and an enormous amount of textual datain the biological and medical literature. New types of scientific and clinical problems require the development of novel supervised and unsupervised learning methods that can use these growing resources.

Furthermore, next generation sequencing technologies are yielding terabyte scale data sets that require novel algorithmic solutions. The goal of this mini-symposium is to present emerging problems and machine learning techniques in computational biology.