An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives. While coping with a limited budget of evaluations, recovering the set of optimal compromise solutions generally requires numerous observations and is less interpretable since this set tends to grow larger with the number of objectives. We thus propose to focus on a specific solution originating from game theory, the Kalai-Smorodinsky solution, which possesses attractive properties. In particular, it ensures equal marginal gains over all objectives. We further make it insensitive to a monotonic transformation of the objectives by considering the objectives in the copula space. A novel tailored algorithm is proposed to search for the solution, in the form of a Bayesian optimization algorithm: sequential sampling decisions are made based on acquisition functions that derive from an instrumental Gaussian process prior. Our approach is tested on three problems with respectively four, six, and ten objectives. The method is available in the package GPGame available on CRAN at https://cran.r-project.org/package=GPGame.

In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.

Modeling buildings' heat dynamics is a complex process which depends on various factors including weather, building thermal capacity, insulation preservation, and residents' behavior. Gray-box models offer a causal inference of those dynamics expressed in few parameters specific to built environments. These parameters can provide compelling insights into the characteristics of building artifacts and have various applications such as forecasting HVAC usage, indoor temperature control monitoring of built environments, etc. In this paper, we present a systematic study of modeling buildings' thermal characteristics and thus derive the parameters of built conditions with a Bayesian approach. We build a Bayesian state-space model that can adapt and incorporate buildings' thermal equations and propose a generalized solution that can easily adapt prior knowledge regarding the parameters. We show that a faster approximate approach using variational inference for parameter estimation can provide similar parameters as that of a more time-consuming Markov Chain Monte Carlo (MCMC) approach. We perform extensive evaluations on two datasets to understand the generative process and show that the Bayesian approach is more interpretable. We further study the effects of prior selection for the model parameters and transfer learning, where we learn parameters from one season and use them to fit the model in the other. We perform extensive evaluations on controlled and real data traces to enumerate buildings' parameter within a 95% credible interval.

Conformal predictive distributions were inspired by the work on predictive distributions inparametric statistics (see, e.g., [7, Chapter 12] and [8]) and first suggested in [14]. As usual, we will refer to algorithms producing conformal predictive distributions as conformal predictive systems (CPS, used in both singular andplural senses). Conformal predictive systems are built on top of traditional prediction algorithms toensure a property of validity usually referred to as calibration in probability [3]. Several versions of the Least Squares Prediction Machine, CPS based on the method of Least Squares, are constructed in [14]. This construction isslightly extended to cover ridge regression and then further extended to nonlinear settings by applying the kernel trick in [12]. However, even after this extension the method is not fully adaptive, even for a universal kernel. As explained in [12, Section 7], the universality of the kernel shows in the ability of the predictive distribution function to take any shape; however, the CPS is still inflexible in that the shape does not depend, or depends weakly, on the test object. Formany base algorithms full CPS (like full conformal predictors in general) are computationally inefficient, and [13] define and study computationally efficient versionsof CPS, namely split-conformal predictive systems (SCPS) and 1 cross-conformal predictive systems (CCPS).

As the popularity of home automation and the cost of electricity grow around the world, energy conservation has become a higher priority for many consumers. With a number of smart meter devices available for your home, you can now measure and record overall household power draw, and then with the output of a machine learning model, accurately predict individual appliance behavior simply by analyzing meter data. For example, your electric utility provider might send you a message if it can reasonably assess that you left your refrigerator door open, or if the irrigation system suddenly came on at an odd time of day. In this post, you'll learn how to accurately identify home appliances' (e.g. Once the algorithm identifies an appliance's operating status, we can then build out a few more applications.

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy.

Natural gas is the most important energy source in Italy: it fuels thermoelectric power plants, industrial facilities and domestic heating. Gas demand forecasting is a critical task for any energy provider as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of Italian daily residential gas demand is studied. Five predictors are developed and compared: Ridge Regression, Gaussian Process, k-Nearest Neighbour, Artificial Neural Network, and Torus Model. Preprocessing and feature selection are also discussed in detail. Concerning the prediction error, a theoretical bound on the best achievable root mean square error is worked out assuming ideal conditions, except for the inaccuracy of meteorological temperature forecasts, whose effects are properly propagated. The best predictors, namely the Artificial Neural Network and the Gaussian Process, achieve an RMSE which is twice the performance limit, suggesting that precise predictions of residential gas demand can be achieved at country level.

Abstract--Early detection of incipient faults is of vital importance toreducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MCdropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MCdropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types. I. INTRODUCTION Building faults whose impact are less perceivable and/or hinder regular operations are called soft faults [21], [32]. These soft faults, especially in their incipient phase, are hard to detect as their signatures are not generally obvious (due to their magnitudes) and are lurking under measurement/system noise or feedback control actions [10], [27]. Nevertheless, they will impact energy consumption, system performance, and maintenance costs adversely in the long-run if left undetected and unattended [14].

A big trend in AI is the transition from cloud to edge computing. Benefits of this approach can include faster results, greater security, and more flexibility. But how far can you push this model? Seattle-based startup Xnor is certainly right at the bleeding-edge. This week the company unveiled a prototype AI camera that runs entirely off solar power -- no battery or external power source required.

Amgen's drug discovery group is a few blocks beyond that. Until recently, Barzilay, one of the world's leading researchers in artificial intelligence, hadn't given much thought to these nearby buildings full of chemists and biologists. But as AI and machine learning began to perform ever more impressive feats in image recognition and language comprehension, she began to wonder: could it also transform the task of finding new drugs? The problem is that human researchers can explore only a tiny slice of what is possible. It's estimated that there are as many as 1060 potentially drug-like molecules--more than the number of atoms in the solar system. But traversing seemingly unlimited possibilities is what machine learning is good at. Trained on large databases of existing molecules and their properties, the programs can explore all possible related molecules.