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.

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.

W e propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS). The first test is based on a large deviation bound for the test statistic, while the second is based on the asymptotic distribution of this statistic.

Propagation based methods are developed and rigorously assessed.

The problem of reconstructing a surface from a set of points frequently arises in computer graphics.

Cooperative competitive networks are believed to play a central role in cortical processing and have been shown to exhibit a wide set of useful computational properties. We propose a VLSI implementation of a spiking cooperative competitive networkand show how it can perform context dependent computation both in the mean firing rate domain and in spike timing correlation space. In the mean rate case the network amplifies the activity of neurons belonging to the selected stimulus and suppresses the activity of neurons receiving weaker stimuli. In the event correlation case, the recurrent network amplifies with a higher gain the correlation betweenneurons which receive highly correlated inputs while leaving the mean firing rate unaltered. We describe the network architecture and present experimental datademonstrating its context dependent computation capabilities.

VM) attempt to learn low-density separators by maximizing the margin over labeled and unlabeled examples. The associated optimizationproblem is non-convex.

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.

Bayesian method applied to these models is an attractive approach, used successfully in applications ranging from acoustics t0 bioinformatics.

We present a learning algorithm for undiscounted reinforcement learning. Our interest lies in bounds for the algorithm's online performance after some finite number of steps. In the spirit of similar methods already successfully applied for the exploration-exploitation tradeoff in multi-armed bandit problems, we use upper confidence bounds to show that our UCRL algorithm achieves logarithmic online regret in the number of steps taken with respect to an optimal policy.