Local and global sparse Gaussian process approximations

proximations, in that they try to summarize all the training data via a small set ... ance matrix, and an O(N2) cost per test case for pre- diction. I...

1 downloads 0 Views 895KB Size

Recommend Documents

Aki Vehtari. Department of Biomedical Engineering and Computational Science. Helsinki University of Technology. 02015 TKK, Finland. Abstract. Much recent work has concerned sparse ap- proximations to speed up the Gaussian pro- cess regression from th

Computer Science and Statistics Depts. Purdue University. West Lafayette, IN 47907, USA. Abstract ... and researchers write papers collaboratively and cre- ate co-author networks. Given network datasets with ... The latent classes provide building bl

For exam- ple, the network nodes are interdependent in- stead of independent of each other, and the data are known to be very noisy (e.g., miss- ing edges). .... −n2/2 det(K). −n/2 det(G). −n/2 exp{−. 1. 2 tr(K. −1MG. −1M. ⊤. )} (8) whe

model for robot model-based control on a Barrett WAM robot arm. Keywords: Robotics ... tracking control and real-time learning is demonstrated on a Barrett whole arm manipulator (WAM) [7]. We can show that its ... hydraulic tubes, complex friction, g

for sequence annotation (Altun et al., 2004) and prostate cancer prediction (Chu et al., 2005), EP for ..... 5. 6. 7. 8. 9. Figure3: Gaussian Process Classification: Prior, Likelihood and exact Posterior: Nine num- bered quadrants show posterior obta

Jul 27, 2015 - problem, in a single–terminal setup, under linear decoding, but ..... density function (pdf), the formulation in (9) can be extended as follows. .... ∑S. Tr{XS } subject to [ R−1 + g2 σ2w. D⊤. S QDS − D⊤. S YDS. IK. IK. XS

Feb 4, 2014 - a mixture model, utilizing ME to fit the data makes inference (unneccesarily) complicated. Hence, we are interested in a probabilistic model that captures the best of both worlds without making the mixture as- sumption: A generative mod

Max Planck Institute for Biological Cybernetics. Spemannstraße 38, 72076 Tübingen, Germany. {duy,jan.peters,matthias.seeger}@tuebingen.mpg.de. Abstract. Learning in real-time applications, e.g., online approximation of the inverse dy- namics model

that there are matrices A where every e-approximation for Max requires l~((log n)/62) many positive q~. ... The nodes of the tree are legal positions; the arcs of the tree stand for the moves, The root is the current ... In the game of chess there ar

Feb 13, 2014 - Abstract. The accurate prediction of time-changing vari- ances is an important task in the modeling of fi- nancial data. Standard econometric models are often limited as they assume rigid functional re- lationships for the variances. M

At times, we act in accordance with OUf core values and ideals. .... (Baldwin & Holmes, 1987; Davis. 227. Local and Global Evaluations as e- b- ts a-. -er, ..... pants are led to adopt a particular processing orientation on one task , the primed cogn

kurtosis n i n vi vmean. 4/ i n vi vmean. 2 2. 3, where n is the number of sample points in the trace, vi is the voltage measured at the ith sample point, and vmean is the mean value of the. DeWeese and Zador•Network Synchrony in Auditory Cortex. J

Abstract. This paper considers elimination algorithms for sparse matri- ces over finite fields. We mostly focus on computing the rank, because it raises the same challenges as solving linear systems, while being slightly simpler. We developed a new s

Nov 9, 2011 - Foundation (NSF) under Grants CCF-1117545 and CCF-0728893; the Army Research Office (ARO) under Grant 58110-MA-II and Grant 60219-MA; and the Office of Naval ..... which measures the “separation” between tree-structured approximatio

Sep 26, 2012 - The objective is to calculate the probability, PF, that a device will fail when its inputs, x, are randomly distributed with probability density, p (x), e.g., the probability that a device will fracture when subject to varying loads. H

role in applications ranging from transport to grid energy storage. However, not knowing a battery's rate of capacity loss or useful life renders the system susceptible to ... Journal of Power Sources 357 (2017) 209e219 ...... cast. We apply the meth

Abstract. We introduce a new regression frame- work, Gaussian process regression networks. (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian pro- cesses. GPRN accommodates inpu

Note that compared with traditional forgings the samples were very small. This meant that there was an inherent limitation on the statistics of the grain size. Too small a sampling area would lead to too few grains in the count, giving rise to large

Sambu Seo Marko Wallat Thore Graepel Klaus Obermayer. Department of Computer Science, .... Regression learning is usually based on the assumption that the data is provided in advance and that the learner is only a passive ..... 5] Mackay, D.J.C., Gau

Dec 12, 2015 - 2Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK ... set of examples, referred to as the observations of a training set. ... (K + σ2. nI) = LLT . The computation of the Cholesky factor is known to be numeri

n2 r2 τ2) ,. (2.12). Tθ θ. = −. 2TpV. √X (1 + τ′2). (2.13). Suppose that there exists single topological tachyon vortex solution of vorticity n (= 0), sat- isfying the boundary conditions (2.7)–(2.8). We try a power-series and a logarithm

arXiv:math/0503491v1 [math.PR] 23 Mar ... and D = D1 + D2. Con- sider a point process ξ on RD = RD1 ×RD2 , which has expectation measure ν and meets three conditions, namely, absolute continuity of ν with a mild restriction on the ..... mate, obt

Learning Fast Approximations of Sparse Coding. Algorithm 2 Coordinate Descent (Li & Osher, 2009) function CoD(X, Z, Wd, S, α). Require: S = I − WT d Wd. Initialize: Z = 0; B = WT d X repeat. ¯Z = hα(B) k = index of largest component of |Z − ¯

Definition. Gaussian processes (GPs) are local approximation tech- niques that model spatial data by placing (and updating) priors on the covariance structures underlying the data. Originally developed for geo-spatial contexts, they are also applicab