This paper presents a sequential randomized lowrank matrix factorization approach for incrementally predicting values of an unknown function at test points using the Gaussian Processes framework. It is well-known that in the Gaussian processes framework, the computational bottlenecks are the inversion of the (regularized) kernel matrix and the computation of the hyper-parameters defining the ke...

The M-estimate of a linear observation model has many important engineering applications such as identifying a linear system under non-Gaussian noise. Batch algorithms based on the EM algorithm or the iterative reweighted least squares algorithm have been widely adopted. In recent years, several sequential algorithms have been proposed. In this paper, we propose a family of sequential algorithm...

State-space models provide an important body of techniques for analyzing time-series, but their use requires estimating unobserved states. The optimal estimate of the state is its conditional expectation given the observation histories, and computing this expectation is hard when there are nonlinearities. Existing filtering methods, including sequential Monte Carlo, tend to be either inaccurate...

We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and highdimensional, collecting enough representative data samples becomes difficult. We present a framework that uses active learning to help with the sequential gathering of data samples, using information-theoretic criteria to find op...

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical ...

In this paper, we establish the existence of teamoptimal policies for static teams and a class of sequential dynamic teams. We first consider the static team problems and show the existence of optimal policies under certain regularity conditions on the observation channels by introducing a topology on the set of policies. Then we consider sequential dynamic teams and establish the existence of ...

The distance-dependent Chinese Restaurant Process (dd-CRP) is a flexible class of distributions over partitions which was recently introduced by [1, 2]. In their description and experiments Blei and Frazier focus on the sequential setting such as clustering over time. Their Gibbs sampler, while general in nature, does not explicitly handle the case of non-sequential (also called spatial) cluste...

Pseudonoise code acquisition is investigated for constant hopping rate fast frequency-hopped (FFH)/M -ary frequency-shift keying systems under the effects of white Gaussian noise and band multitone jamming. In particular, serial search acquisition systems based on the traditional multiple-dwell test (up to three dwells) and three novel sequential tests are analyzed and compared. Analytical resu...

Consider sequential packing of unit volume balls in a large cube, in any dimension and with Poisson input. We show after suitable rescaling that the spatial distribution of packed balls tends to that of a Gaussian field in the thermodynamic limit. The results cover related applied models, including ballistic deposition and spatial birth-growth models.

In this paper we present new online algorithms to estimate static parameters in nonlinear non Gaussian state space models. These algorithms rely on online Expectation-Maximization (EM) type algorithms. Contrary to standard Sequential Monte Carlo (SMC) methods recently proposed in the literature, these algorithms do not degenerate over time.