48 accepted papers
Bayesian Optimization (BO) is a popular black-box optimization method consisting of a surrogate model, typically a probabilistic model such as a Gaussian Process (GP) and an Acquisition function (AF). Effective selection of these functions has a strong impact on the optimization process. Existing en…
Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentrali…
In this paper, we propose Online LASSO - a version of LASSO that is configured for streaming data. In standard LASSO, the penalty parameter is typically chosen by cross-validation, a procedure which requires the entire dataset upfront and repeated fitting. The main contribution of this work is in fi…
This paper introduces a novel structure for deep Gaussian processes (DGPs) and a method for determining their depths. The proposed framework enables faster convergence of their parameters and reduces computational cost to optimize them while maintaining performance comparable to that of conventional…
Bayesian optimization (BO) is a popular approach to optimizing costly, black-box functions that rely on a statistical surrogate model of the function to select new query points, balancing exploration and exploitation of the parameter space. Most of the work on BO has focused on the time-invariant se…
Conventionally, it is often assumed that the training and testing data distributions are the same and that all classes in the test set are observed in the training set. However, this assumption may not be true in real-world tasks. In practice, there may be test samples from classes that were unknown…
In this paper, we explore the estimation of directed time-varying graph topologies, which represent evolving relationships among nodes in high-dimensional, interdependent data. We introduce a novel fully Bayesian method based on Gaussian processes, employing random walks to model the time-varying ed…
The inherent adaptability and flexibility of Gaussian processes lie in the capability of their kernel functions to capture diverse data characteristics. Thus, selecting an appropriate kernel function is crucial because an improper choice can detrimentally affect the model’s performance. One way to e…
Detection of anomalies is a common and important problem, especially when anomalies are rare and labels are difficult to acquire. Here we sequentially detect outliers in the outputs of an unknown function, which have been distorted by noise. We model the sequence of outputs by using Yule-Simon proce…
In many machine learning problems, one has to work with data of different types, including continuous, discrete, and categorical data. Further, it is often the case that many of these data are missing from the database. This paper proposes a Gaussian process framework that efficiently captures the i…
In science and engineering, we often deal with signals that are acquired from time-varying systems represented by dynamic graphs. We observe these signals, and the interest is in finding the time-varying topology of the graphs. We propose two Bayesian methods for estimating these topologies without…
During the process of childbirth, fetal distress caused by hypoxia can lead to various abnormalities. Cardiotocography (CTG), which consists of continuous recording of the fetal heart rate (FHR) and uterine contractions (UC), is routinely used for classifying the fetuses as hypoxic or non-hypoxic. I…
Low umbilical artery pH is a marker for neonatal acidosis and is associated with an increased risk for neonatal complications. The phase-rectified signal averaging (PRSA) features have demonstrated superior discriminatory or diagnostic ability and good interpretability in many biomedical application…
Yule-Simon processes are one of the most commonly occurring processes in Nature. These processes generate power laws using a preferential attachment mechanism which can describe a variety of data distributions such as word frequencies, scientific citations, journal publications, income, node connect…
Determining the number of latent variables, or the dimensions of latent states, is a ubiquitous problem in dimension reduction. In this paper, we introduce a novel sequential method that relies on the Bayesian approach to estimate the dimension of a latent space of a Gaussian process latent variable…
The computer-aided interpretation of fetal heart rate (FHR) and uterine contraction (UC) has not been developed well enough for wide use in delivery rooms. The main challenges still lie in the lack of unclear and nonstandard labels for cardiotocography (CTG) recordings, and the timely prediction of…
The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need fo…
In this paper, we develop a new method for estimating the tail-index found in extreme value statistics. Using a fixed quantile, model-selection approach, we derive the posterior distribution of the tail-index marginalizing out the unknown threshold and nuisance parameters. Our marginalized threshold…
Classification with imbalanced data is a common and challenging problem in many practical machine learning problems. Ensemble learning is a popular solution where the results from multiple base classifiers are synthesized to reduce the effect of a possibly skewed distribution of the training set. In…
Identifying uterine contractions with the aid of machine learning methods is necessary vis-á-vis their use in combination with fetal heart rates and other clinical data for the assessment of a fetus wellbeing. In this paper, we study contraction identification by processing noisy signals due to uter…
Regime-switching state-space models (RS-SSMs) are an important class of statistical models that can be used to represent real-world phenomena. Unlike regular state-space models, RS-SSMs allow for dynamic uncertainty in the state transition and observations distributions, making them much more expres…
In this paper, we propose a new approach for analyzing extreme values that are witnessed in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time and, as opposed to modeling just the maximum of a block of observations, we model the conditi…
Convergent cross mapping (CCM) is designed for causal discovery in coupled time series, where Granger causality may not be applicable because of a separability assumption. However, CCM is not robust to observation noise which limits its applicability on signals that are known to be noisy. Moreover,…
The population Monte Carlo (PMC) algorithm is a popular adaptive importance sampling (AIS) method used for approximate computation of intractable integrals. Over the years, many advances have been made in the theory and implementation of PMC schemes. The mixture PMC (M-PMC) algorithm, for instance,…
Convergent cross mapping (CCM) is designed for causal discovery between coupled time series for which Granger's method for detecting causality is shown to be unreliable. The theoretical foundation of CCM is based on state space reconstruction, and therefore, for the accuracy of its results, the qual…
In this paper, we present a technique whereby passive tags can track each other in a backscattering tag-to-tag network (BTTN). In such a network, passive tags without any on-board radio transceivers communicate directly with each other by backscattering an external excitation signal. First, we expla…
In this paper, we propose a new approach for analyzing extreme values such as large losses in financial markets. Our goal is to compute the predictive distribution of extreme events that are clustered in time. We apply a stochastic parametrization of the generalized extreme value distribution to mod…
Adaptive importance sampling (AIS) methods are a family of algorithms which can be used to approximate Bayesian posterior distributions. Many AIS algorithms exist in the literature, where the differences arise in the manner by which the proposal distribution is adapted at each iteration. The adaptiv…
In this paper, we propose a novel and simple method for discovery of Granger causality from noisy time series using Gaussian processes. More specifically, we adopt the concept of Granger causality, but instead of using autoregressive models for establishing it, we work with Gaussian processes. We sh…
Yule-Simon processes are preferential attachment processes that can be represented as urn processes where balls are added to a growing number of urns and where a new ball is placed in an urn with probability that is proportional to the number of balls in the urn. In this paper, we consider Yule-Simo…
In this paper we propose a probabilistic framework for proficiency self-assessment of autonomous systems. We define proficiency as a mathematical concept, i.e., as a metric that depends on a variety of factors. This concept allows for assessment of the degree of completion of a given task by a syste…
We have developed a type of RFID tags that can communicate with each other directly if there is an RF signal in their environment to support backscattering. These tags are passive and they can form a tag-to-tag network. Our tags communicate by what we refer to as multiphase probing. With this techni…
Opinion dynamics and its understanding in social networks is an emerging field of research in recent years. Existing work mainly considers direct exchanges of opinions among agents under certain conditions. This paper addresses a problem where the agents of a network make and exchange decisions repe…
In localization tasks, one typically assumes a statistical model of the observations, where the model quantifies the observations by exploiting interrelationships based on geometry. These models might incorporate unknown parameters that, in general, are functions of space. In this article, we propos…
In this paper, we propose an application of non-parametric Bayesian (NPB) models to classification of fetal heart rate recordings. More specifically, the models are used to discriminate between fetal heart rate recordings that belong to fetuses that may have adverse asphyxia outcomes and those that…
We present a novel Rao-Blackwellized multiple particle filtering method for inference of correlated latent states observed via nonlinear functions. We adopt a state-space framework and model the dynamic correlated states using a mixing matrix, embedded in white Gaussian noise. The critical challenge…
We address the learning problem for infinite state Hidden Markov Models (HMMs) with preferential attachment dynamics. Preferential attachment describes a “rich get richer” process causing the HMM self transition probabilities to be proportional to the number of previous self transitions. Furthermore…
In many problems of signal processing, an important task is the classification of data. A group of methods that has attracted much interest for this purpose are the nonparametric Bayesian methods, and in particular, those based on the Dirichlet process. A useful metaphor for various generalizations…
In this paper, we propose to analyze fetal heart rate (FHR) signals by hierarchical Dirichlet process (HDP) mixture models. We investigate whether the clustering results of real-world FHR time series obtained by these models are informative in terms of determining the health status of a fetus. The F…
Particle filtering is a widely used sequential methodology that approximates probability distributions by using discrete random measures composed of weighted particles. A large number of particles improves the quality of the approximation but increases the computational requirements. Although there…
Opinion dynamics in social networks has been widely studied in recent years, mostly by considering exchanges of opinions among neighboring agents. This paper addresses a scenario where the agents make decisions repeatedly on two hypotheses and where agents only exchange decisions. Motivated by the B…
In this paper, we consider state-space models where the latent processes represent correlated mixtures of fractional Gaussian processes embedded in white Gaussian noises. The observed data are nonlinear functions of the latent states. The fractional Gaussian processes have interesting properties inc…
In this paper, we consider the problem of social learning in a network of agents where the agents make decisions onK hypotheses sequentially and broadcast their decisions to others. Each agent in the system has a private observation that is generated by one of the hypotheses. All the observations ar…
Distributed filtration of state-space models with sensor networks assumes knowledge of a model of the data-generating process. However, this assumption is often violated in practice, as the conditions vary from node to node and are usually only partially known. In addition, the model may generally b…
Particle filtering has been widely accepted as an important methodology for processing data represented by state-space models characterized by nonlinearities and/or non-Gaussianities. It is also well documented that particle filtering deteriorates quickly in performance when the dimension of the tra…
This paper presents an analysis on optimal mobile sensor configuration for multiple-target-tracking (MTT) with Received-Signal-Strength-Indicator (RSSI) based measurements. The analysis is based on the underlying assumption that the complexity of this inherently high-dimensional problem is reduced b…
This paper considers inference on the widely used state-space models described by hidden ARMA state processes of unknown order observed via non-linear functions of the states. We propose a particle filtering method for sequentially inferring the unknown ARMA time-series by Rao-Blackwellization of al…
We investigate the problem of real-time self-tracking of tagged objects in a new system with low-cost “smart” tags. These tiny and battery-less devices will play a pivotal role in the infrastructure of the Internet of Things (IoT). With capabilities of low-power computation and tag-to-tag backscatte…