Auto-tuning deep forest for shear stiffness prediction of headed stud connectors

The shear stiffness of headed stud connector is a critical parameter for the calculation of deflection and inter-facial shear force for steel-concrete composite structure. Thus, this study presented a promising data-driven model auto-tuning Deep Forest (ATDF) to precisely predict the stud shear stiffness, where the novel Deep For-est algorithm is integrated with the Sequential Model-Based Optimization method to achieve automatic hyper -parameter optimization. Six variables having causal relationships with shear stiffness were extracted via mechanism and model analysis, including the effect of weld collar that cannot be considered in existing models and subsequently constituting a database of 425 push-out tests. Then the ATDF model was trained by combining the advantages of deep learning, ensemble learning, and auto-tuning techniques. It was approved to significantly outperform representative benchmark models with R values of 0.91 and 0.87 for training and testing sets. The ATDF was subjected to attribute importance analysis, which quantified the stud diameter and concrete elastic modulus as the most significant variables for shear stiffness, with the stud elastic modulus having the minimal effect. The model uncertainty of ATDF was further evaluated, revealing that it had the lowest bias and variability than those in existing empirical or semi-empirical models. Finally, the reliability analysis was conducted and the partial factors of ATDF under specified target reliability were derived.

Ensembles for Sequence Learning

Christos Dimitrakakis

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement learning problems. In both types of problems, even after having observed a certain amount of data, we are often faced with uncertainty as to which hypothesis is correct among all the possible ones. However, in many methods for both supervised and for reinforcement learning problems this uncertainty is ignored, in the sense that there is a single solution selected out of the whole of the hypothesis space. Apart from the classical solution of analytical Bayesian formulations, ensemble methods offer an alternative approach to representing this uncertainty. This is done simply through maintaining a set of alternative hypotheses. The sequential supervised problem considered is that of automatic speech recognition using hidden Markov models. The application of ensemble methods to the problem represents a challenge in itself, since most such methods can not be readily adapted to sequential learning tasks. This thesis proposes a number of different approaches for applying ensemble methods to speech recognition and develops methods for effective training of phonetic mixtures with or without access to phonetic alignment data. Furthermore, the notion of expected loss is introduced for integrating probabilistic models with the boosting approach. In some cases substantial improvements over the baseline system are obtained. In reinforcement learning problems the goal is to act in such a way as to maximise future reward in a given environment. In such problems uncertainty becomes important since neither the environment nor the distribution of rewards that result from each action are known. This thesis presents novel algorithms for acting nearly optimally under uncertainty based on theoretical considerations. Some ensemble-based representations of uncertainty (including a fully Bayesian model) are developed and tested on a few simple tasks resulting in performance comparable with the state of the art. The thesis also draws some parallels between a proposed representation of uncertainty based on gradient-estimates and on ``prioritised sweeping'' and between the application of reinforcement learning to controlling an ensemble of classifiers and classical supervised ensemble learning methods.

École Polytechnique Fédérale de Lausanne2006

Robust Multivariate and Nonlinear Time Series Models

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. Autocorrelation is one of the fundamental aspects of time series modeling. However, traditional linear models, that arise from a strong observed autocorrelation in many financial and econometric time series data, are at times unable to capture the rather nonlinear relationship that characterizes many time series data. This necessitates the study of nonlinear models in analyzing such time series. The class of bilinear models is one of the simplest nonlinear models. These models are able to capture temporary erratic fluctuations that are common in many financial returns series and thus, are of tremendous interest in financial time series analysis. Another aspect of time series analysis is homoscedasticity versus heteroscedasticity. Many time series data, even after differencing, exhibit heteroscedasticity. Thus, it becomes important to incorporate this feature in the associated models. The class of conditional heteroscedastic autoregressive (ARCH) models and its variants form the primary backbone of conditional heteroscedastic time series models. Robustness is a highly underrated feature of most time series applications and models that are presently in use in the industry. With an ever increasing amount of information available for modeling, it is not uncommon for the data to have some aberrations within itself in terms of level shifts and the occasional large fluctuations. Conventional methods like the maximum likelihood and least squares are well known to be highly sensitive to such contaminations. Hence, it becomes important to use robust methods, especially in this age with high amounts of computing power readily available, to take into account such aberrations. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of robust parameter estimation of some simple vector and nonlinear time series models. More precisely, we will briefly study some prominent linear and nonlinear models in the time series literature and apply the robust S-estimator in estimating parameters of some simple models like the vector autoregressive (VAR) model, the (0, 0, 1, 1) bilinear model and a simple conditional heteroscedastic bilinear model. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.

EPFL2010

Ensembles for Sequence Learning

Christos Dimitrakakis

École Polytechnique Fédérale de Lausanne2006

Robust Multivariate and Nonlinear Time Series Models

EPFL2010