String and Membrane Gaussian Processes

In this paper we introduce a novel framework for making exact nonparametric Bayesian inference on latent functions, that is particularly suitable for Big Data tasks. Firstly, we introduce a class of stochastic processes we refer to as string Gaussian processes (string GPs), which are not to be mistaken for Gaussian processes operating on text. We construct string GPs so that their finite-dimensional marginals exhibit suitable local conditional independence structures, which allow for scalable, distributed, and flexible nonparametric Bayesian inference, without resorting to approximations, and while ensuring some mild global regularity constraints. Furthermore, string GP priors naturally cope with heterogeneous input data, and the gradient of the learned latent function is readily available for explanatory analysis. Secondly, we provide some theoretical results relating our approach to the standard GP paradigm. In particular, we prove that some string GPs are Gaussian processes, which provides a complementary global perspective on our framework. Finally, we derive a scalable and distributed MCMC scheme for supervised learning tasks under string GP priors. The proposed MCMC scheme has computational time complexity $\mathcal{O}(N)$ and memory requirement $\mathcal{O}(dN)$, where $N$ is the data size and $d$ the dimension of the input space. We illustrate the efficacy of the proposed approach on several synthetic and real-world datasets, including a dataset with $6$ millions input points and $8$ attributes.Comment: To appear in the Journal of Machine Learning Research (JMLR), Volume 1

Shared Control Policies and Task Learning for Hydraulic Earth-Moving Machinery

This thesis develops a shared control design framework for improving operator efficiency and performance on hydraulic excavation tasks. The framework is based on blended shared control (BSC), a technique whereby the operator’s command input is continually augmented by an assistive controller. Designing a BSC control scheme is subdivided here into four key components. Task learning utilizes nonparametric inverse reinforcement learning to identify the underlying goal structure of a task as a sequence of subgoals directly from the demonstration data of an experienced operator. These subgoals may be distinct points in the actuator space or distributions overthe space, from which the operator draws a subgoal location during the task. The remaining three steps are executed on-line during each update of the BSC controller. In real-time, the subgoal prediction step involves utilizing the subgoal decomposition from the learning process in order to predict the current subgoal of the operator. Novel deterministic and probabilistic prediction methods are developed and evaluated for their ease of implementation and performance against manually labeled trial data. The control generation component involves computing polynomial trajectories to the predicted subgoal location or mean of the subgoal distribution, and computing a control input which tracks those trajectories. Finally, the blending law synthesizes both inputs through a weighted averaging of the human and control input, using a blending parameter which can be static or dynamic. In the latter case, mapping probabilistic quantities such as the maximum a posteriori probability or statistical entropy to the value of the dynamic blending parameter may yield a more intelligent control assistance, scaling the intervention according to the confidence of the prediction. A reduced-scale (1/12) fully hydraulic excavator model was instrumented for BSC experimentation, equipped with absolute position feedback of each hydraulic actuator. Experiments were conducted using a standard operator control interface and a common earthmoving task: loading a truck from a pile. Under BSC, operators experienced an 18% improvement in mean digging efficiency, defined as mass of material moved per cycle time. Effects of BSC vary with regard to pure cycle time, although most operators experienced a reduced mean cycle time

Mining Text and Time Series Data with Applications in Finance

Finance is a field extremely rich in data, and has great need of methods for summarizing and understanding these data. Existing methods of multivariate analysis allow the discovery of structure in time series data but can be difficult to interpret. Often there exists a wealth of text data directly related to the time series. In this thesis it is shown that this text can be exploited to aid interpretation of, and even to improve, the structure uncovered. To this end, two approaches are described and tested. Both serve to uncover structure in the relationship between text and time series data, but do so in very different ways. The first model comes from the field of topic modelling. A novel topic model is developed, closely related to an existing topic model for mixed data. Improved held-out likelihood is demonstrated for this model on a corpus of UK equity market data and the discovered structure is qualitatively examined. To the authors’ knowledge this is the first attempt to combine text and time series data in a single generative topic model. The second method is a simpler, discriminative method based on a low-rank decomposition of time series data with constraints determined by word frequencies in the text data. This is compared to topic modelling using both the equity data and a second corpus comprising foreign exchange rates time series and text describing global macroeconomic sentiments, showing further improvements in held-out likelihood. One example of an application for the inferred structure is also demonstrated: construction of carry trade portfolios. The superior results using this second method serve as a reminder that methodological complexity does not guarantee performance gains

Finite-dimensional nonparametric priors: theory and applications

The investigation of flexible classes of discrete prior has been an active research line in Bayesian statistics. Several contributions were devoted to the study of nonparametric priors, including the Dirichlet process, the Pitman–Yor process and normalized random measures with independent increments (NRMI). In contrast, only few finite-dimensional discrete priors are known, and even less come with sufficient theoretical guarantees. In this thesis we aim at filling this gap by presenting several novel general classes of parametric priors closely connected to well-known infinite-dimensional processes, which are recovered as limiting case. A priori and posteriori properties are extensively studied. For instance, we determine explicit expressions for the induced random partition, the associated urn schemes and the posterior distributions. Furthermore, we exploit finite-dimensional approximations to facilitate posterior computations in complex models beyond the exchangeability framework. Our theoretical and computational findings are employed in a variety of real statistical problems, covering toxicological, sociological, and marketing applications

Statistical inference for periodic and partially observable poisson processes

This thesis develops practical Bayesian estimators and exploration methods for count data collected by autonomous robots with unreliable sensors for long periods of time. It addresses the problems of drawing inferences from temporally incomplete and unreliable count data. This thesis contributes statistical models with spectral analysis which are able to capture the periodic structure of count data on extended temporal scales from temporally sparse observations. It is shown how to use these patterns to i) predict the human activity level at particular times and places and ii) categorize locations based on their periodic patterns. The second main contribution is a set of inference methods for a Poisson process which takes into account the unreliability of the detection algorithms used to count events. Two tractable approximations to the posterior of such Poisson processes are presented to cope with the absence of a conjugate density. Variations of these processes are presented, in which (i) sensors are uncorrelated, (ii) sensors are correlated, (iii) the unreliability of the observation model, when built from data, is accounted for. A simulation study shows that these partially observable Poisson process (POPP) filters correct the over- and under-counts produced by sensors. The third main contribution is a set of exploration methods which brings together the spectral models and the POPP filters to drive exploration by a mobile robot for a series of nine-week deployments. This leads to (i) a labelled data set and (ii) solving an exploration exploitation trade-off: the robot must explore to find out where activities congregate, so as to then exploit that by observing as many activities

Towards Trust and Transparency in Deep Learning Systems through Behavior Introspection & Online Competency Prediction

Deep neural networks are naturally “black boxes”, offering little insight into how or why they make decisions. These limitations diminish the adoption likelihood of such systems for important tasks and as trusted teammates. We employ introspective techniques to abstract machine activation patterns into human-interpretable strategies and identify relationships between environmental conditions (why), strategies (how), and performance (result) on both a deep reinforcement learning two-dimensional pursuit game application and image-based deep supervised learning obstacle recognition application. Pursuit-evasion games have been studied for decades under perfect information and analytically-derived policies for static environments. We incorporate uncertainty in a target’s position via simulated measurements and demonstrate a novel continuous deep reinforcement learning approach against speed-advantaged targets. The resulting approach was tested under many scenarios and performance exceeded that of a baseline course-aligned strategy. We manually observed separation of learned pursuit behaviors into strategy groups and manually hypothesized environmental conditions that affected performance. These manual observations motivated automation and abstraction of conditions, performance and strategy relationships. Next, we found that deep network activation patterns could be abstracted into human-interpretable strategies for two separate deep learning approaches. We characterized machine commitment by the introduction of a novel measure and revealed significant correlations between machine commitment, strategies, environmental conditions, and task performance. As such, we motivated online exploitation of machine behavior estimation for competency-aware intelligent systems. And finally, we realized online prediction capabilities for conditions, strategies, and performance. Our competency-aware machine learning approach is easily portable to new applications due to its Bayesian nonparametric foundation, wherein all inputs are compactly transformed into the same compact data representation. In particular, image data is transformed into a probability distribution over features extracted from the data. The resulting transformation forms a common representation for comparing two images, possibly from different types of sensors. By uncovering relationships between environmental conditions (why), machine strategies (how), & performance (result) and by giving rise to online estimation of machine competency, we increase transparency and trust in machine learning systems, contributing to the overarching explainable artificial intelligence initiative.

Generative modeling of dynamic visual scenes

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 301-312).Modeling visual scenes is one of the fundamental tasks of computer vision. Whereas tremendous efforts have been devoted to video analysis in past decades, most prior work focuses on specific tasks, leading to dedicated methods to solve them. This PhD thesis instead aims to derive a probabilistic generative model that coherently integrates different aspects, notably appearance, motion, and the interaction between them. Specifically, this model considers each video as a composite of dynamic layers, each associated with a covering domain, an appearance template, and a flow describing its motion. These layers change dynamically following the associated flows, and are combined into video frames according to a Z-order that specifies their relative depth-order. To describe these layers and their dynamic changes, three major components are incorporated: (1) An appearance model describes the generative process of the pixel values of a video layer. This model, via the combination of a probabilistic patch manifold and a conditional Markov random field, is able to express rich local details while maintaining global coherence. (2) A motion model captures the motion pattern of a layer through a new concept called geometric flow that originates from differential geometric analysis. A geometric flow unifies the trajectory-based representation and the notion of geometric transformation to represent the collective dynamic behaviors persisting over time. (3) A partial Z-order specifies the relative depth order between layers. Here, through the unique correspondence between equivalent classes of partial orders and consistent choice functions, a distribution over the spaces of partial orders is established, and inference can thus be performed thereon. The development of these models leads to significant challenges in probabilistic modeling and inference that need new techniques to address. We studied two important problems: (1) Both the appearance model and the motion model rely on mixture modeling to capture complex distributions. In a dynamic setting, the components parameters and the number of components in a mixture model can change over time. While the use of Dirichlet processes (DPs) as priors allows indefinite number of components, incorporating temporal dependencies between DPs remains a nontrivial issue, theoretically and practically. Our research on this problem leads to a new construction of dependent DPs, enabling various forms of dynamic variations for nonparametric mixture models by harnessing the connections between Poisson and Dirichlet processes. (2) The inference of partial Z-order from a video needs a method to sample from the posterior distribution of partial orders. A key challenge here is that the underlying space of partial orders is disconnected, meaning that one may not be able to make local updates without violating the combinatorial constraints for partial orders. We developed a novel sampling method to tackle this problem, which dynamically introduces virtual states as bridges to connect between different parts of the space, implicitly resulting in an ergodic Markov chain over an augmented space. With this generative model of visual scenes, many vision problems can be readily solved through inference performed on the model. Empirical experiments demonstrate that this framework yields promising results on a series of practical tasks, including video denoising and inpainting, collective motion analysis, and semantic scene understanding.by Dahua Lin.Ph.D

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio