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In 2013, the Robotics information, please visit the IAAI-13 iaai13 icwsm13 track is particularly interested in website at www.aaai.org/iaai13.

ICWSM-13 will be held 8-11 July at Conference. FLAIRS 2012 will be held MIT, Cambridge, MA USA May 22-24, 2012 at the TradeWinds Fifteenth International Conference Island Resort, St. Pete Beach, FL ICAPS 2013 will be held Intelligence Conference. IJCAI-13 will be held August 3-URL: www.iccbr.org ICINCO 2013 will be near Washington, DC USA. Thirty-Fifth Annual Conference of held July 27-31, 2014, in Quebec City, The Fifth Conference on Artificial the Cognitive Science Society.

Mirror descent with an entropic regularizer is known to achieve shifting regret bounds that are logarithmic in the dimension. This is done using either a carefully designed projection or by a weight sharing technique. Via a novel unified analysis, we show that these two approaches deliver essentially equivalent bounds on a notion of regret generalizing shifting, adaptive, discounted, and other related regrets. Our analysis also captures and extends the generalized weight sharing technique of Bousquet and Warmuth, and can be refined in several ways, including improvements for small losses and adaptive tuning of parameters.

Our central goal is to quantify the long-term progression of pediatric neurological diseases, such as a typical 10-15 years progression of child dystonia. To this purpose, quantitative models are convincing only if they can provide multi-scale details ranging from neuron spikes to limb biomechanics. The models also need to be evaluated in hyper-time, i.e. significantly faster than real-time, for producing useful predictions. We designed a platform with digital VLSI hardware for multi-scale hyper-time emulations of human motor nervous systems. The platform is constructed on a scalable, distributed array of Field Programmable Gate Array (FPGA) devices. All devices operate asynchronously with 1 millisecond time granularity, and the overall system is accelerated to 365x real-time. Each physiological component is implemented using models from well documented studies and can be flexibly modified. Thus the validity of emulation can be easily advised by neurophysiologists and clinicians. For maximizing the speed of emulation, all calculations are implemented in combinational logic instead of clocked iterative circuits. This paper presents the methodology of building FPGA modules in correspondence to components of a monosynaptic spinal loop. Results of emulated activities are shown. The paper also discusses the rationale of approximating neural circuitry by organizing neurons with sparse interconnections. In conclusion, our platform allows introducing various abnormalities into the neural emulation such that the emerging motor symptoms can be analyzed. It compels us to test the origins of childhood motor disorders and predict their long-term progressions.

We derive a novel norm that corresponds to the tightest convex relaxation of sparsity combined with an $\ell_2$ penalty. We show that this new norm provides a tighter relaxation than the elastic net, and is thus a good replacement for the Lasso or the elastic net in sparse prediction problems. But through studying our new norm, we also bound the looseness of the elastic net, thus shedding new light on it and providing justification for its use.

Numerical integration is an key component of many problems in scientific computing, statistical modelling, and machine learning. Bayesian Quadrature is a model-based method for numerical integration which, relative to standard Monte Carlo methods, offers increased sample efficiency and a more robust estimate of the uncertainty in the estimated integral. We propose a novel Bayesian Quadrature approach for numerical integration when the integrand is non-negative, such as the case of computing the marginal likelihood, predictive distribution, or normalising constant of a probabilistic model. Our approach approximately marginalises the quadrature model's hyperparameters in closed form, and introduces an active learning scheme to optimally select function evaluations, as opposed to using Monte Carlo samples. We demonstrate our method on both a number of synthetic benchmarks and a real scientific problem from astronomy.

We estimate the sample complexity of a recent robust estimator for a generalized version of the inverse covariance matrix. This estimator is used in a convex algorithm for robust subspace recovery (i.e., robust PCA). Our model assumes a sub-Gaussian underlying distribution and an i.i.d.~sample from it. Our main result shows with high probability that the norm of the difference between the generalized inverse covariance of the underlying distribution and its estimator from an i.i.d.~sample of size $N$ is of order $O(N^{-0.5+\eps})$ for arbitrarily small $\eps>0$ (affecting the probabilistic estimate); this rate of convergence is close to one of direct covariance and inverse covariance estimation, i.e., $O(N^{-0.5})$. Our precise probabilistic estimate implies for some natural settings that the sample complexity of the generalized inverse covariance estimation when using the Frobenius norm is $O(D^{2+\delta})$ for arbitrarily small $\delta>0$ (whereas the sample complexity of direct covariance estimation with Frobenius norm is $O(D^{2})$). These results provide similar rates of convergence and sample complexity for the corresponding robust subspace recovery algorithm, which are close to those of PCA. To the best of our knowledge, this is the only work analyzing the sample complexity of any robust PCA algorithm.

We consider the problem of performing efficient sum-product computations in an online setting over a tree. A natural application of our methods is to compute the marginal distribution at a vertex in a tree-structured Markov random field. Belief propagation can be used to solve this problem, but requires time linear in the size of the tree, and is therefore too slow in an online setting where we are continuously receiving new data and computing individual marginals. With our method we aim to update the data and compute marginals in time that is no more than logarithmic in the size of the tree, and is often significantly less. We accomplish this via a hierarchical covering structure that caches previous local sum-product computations. Our contribution is threefold: we i) give a linear time algorithm to find an optimal hierarchical cover of a tree; ii) give a sum-productlike algorithmto efficiently compute marginals with respect to this cover; and iii) apply "i" and "ii" to find an efficient algorithm with a regret bound for the online allocation problem in a multi-task setting.

In regression problems over $\real^d$, the unknown function $f$ often varies more in some coordinates than in others. We show that weighting each coordinate $i$ with the estimated norm of the $i$th derivative of $f$ is an efficient way to significantly improve the performance of distance-based regressors, e.g. kernel and $k$-NN regressors. We propose a simple estimator of these derivative norms and prove its consistency. Moreover, the proposed estimator is efficiently learned online.