We consider the scenario where training and test data are drawn from different distributions, commonly referred to as sample selection bias. Most algorithms for this setting try to first recover sampling distributions and then make appropriate correctionsbased on the distribution estimate. We present a nonparametric method which directly produces resampling weights without distribution estimation. Ourmethod works by matching distributions between training and testing sets in feature space. Experimental results demonstrate that our method works well in practice.

' These authors contributed equally to this work .

The neurons of the neocortex communicate by asynchronous events called action potentials (or'spikes'). However, for simplicity of simulation, most models of processing by cortical neural networks have assumed that the activations of their neurons can be approximated by event rates rather than taking account of individual spikes.The obstacle to exploring the more detailed spike processing of these networks has been reduced considerably in recent years by the development of hybrid analog-digital Very-Large Scale Integrated (hVLSI) neural networks composed ofspiking neurons that are able to operate in real-time. In this paper we describe sucha hVLSI neural network that performs an interesting task of selective attentional processing that was previously described for a simulated'pointer-map' rate model by Hahnloser and colleagues. We found that most of the computational features of their rate model can be reproduced in the spiking implementation; but, that spike-based processing requires a modification of the original network architecture inorder to memorize a previously attended target.

The presented denoising algorithm is based on a graph-based diffusion process of the point sample. We analyze this diffusion process using recent results aboutthe convergence of graph Laplacians. In the experiments we show that our method is capable of dealing with nontrivial high-dimensional noise. Moreover usingthe denoising algorithm as pre-processing method we can improve the results of a semi-supervised learning algorithm.

First we demonstrate stiucture learning on a toy problem.

Selective attention is the strategy used by biological sensory systems to solve the problem of limited parallel processing capacity: salient subregions of the input stimuliare serially processed, while non-salient regions are suppressed. We present an mixed mode analog/digital Very Large Scale Integration implementation ofa building block for a multi-chip neuromorphic hardware model of selective attention. We describe the chip's architecture and its behavior, when its is part of a multi-chip system with a spiking retina as input, and show how it can be used to implement in real-time flexible models of bottom-up attention.

We present a method for learning a low-dimensional representation which is shared across a set of multiple related tasks. The method builds upon the wellknown 1-normregularization problem using a new regularizer which controls the number of learned features common for all the tasks. We show that this problem is equivalent to a convex optimization problem and develop an iterative algorithm for solving it. The algorithm has a simple interpretation: it alternately performs a supervised and an unsupervised step, where in the latter step we learn commonacross-tasks representationsand in the former step we learn task-specific functions using these representations. We report experiments on a simulated and a real data set which demonstrate that the proposed method dramatically improves the performance relativeto learning each task independently. Our algorithm can also be used, as a special case, to simply select - not learn - a few common features across the tasks.

We rigorously establish a close relationship between message passing algorithms and models of neurodynamics by showing that the equations of a continuous Hopfield networkcan be derived from the equations of belief propagation on a binary Markov random field. As Hopfield networks are equipped with a Lyapunov function, convergenceis guaranteed. As a consequence, in the limit of many weak connections perneuron, Hopfield networks exactly implement a continuous-time variant of belief propagation starting from message initialisations that prevent from running into convergence problems. Our results lead to a better understanding of the role of message passing algorithms in real biological neural networks.

We describe Hidden Semi-Markov Support Vector Machines (SHM SVMs), an extension of HM SVMs to semi-Markov chains. This allows us to predict segmentations ofsequences based on segment-based features measuring properties such as the length of the segment. We propose a novel technique to partition the problem into sub-problems. The independently obtained partial solutions can then be recombined in an efficient way, which allows us to solve label sequence learning problemswith several thousands of labeled sequences. We have tested our algorithm for predicting gene structures, an important problem in computational biology. Results on a well-known model organism illustrate the great potential of SHM SVMs in computational biology.

In this paper we are presenting a novel multivariate analysis method. Our scheme is based on a novel kernel orthonormalized partial least squares (PLS) variant for feature extraction, imposing sparsity constrains in the solution to improve scalability. Thealgorithm is tested on a benchmark of UCI data sets, and on the analysis of integrated short-time music features for genre prediction. The upshot is that the method has strong expressive power even with rather few features, is clearly outperforming the ordinary kernel PLS, and therefore is an appealing method for feature extraction of labelled data.