Current practice in whole time series clustering of residential meter data focuses on aggregated or subsampled load data at the customer level, which ignores day-to-day differences within customers. This information is critical to determine each customer’s suitability to various demand side management strategies that support intelligent power grids and smart energy management. Clustering daily load shapes provides fine-grained information on customer attributes and sources of variation for subsequent models and customer segmentation. In this paper, we apply 11 clustering methods to daily residential meter data. We evaluate their parameter settings and suitability based on 6 generic performance metrics and post-checking of resulting clusters. Finally, we recommend suitable techniques and parameters based on the goal of discovering diverse daily load patterns among residential customers. To the authors’ knowledge, this paper is the first robust comparative review of clustering techniques applied to daily residential load shape time series in the power systems’ literature.

We present a framework for automated analysis and categorization of .onion websites in the darkweb to facilitate analyst situational awareness of new content that emerges from this dynamic landscape. Over the last two years, our team has developed a large-scale darkweb crawling infrastructure called OnionCrawler that acquires new onion domains on a daily basis, and crawls and indexes millions of pages from these new and previously known .onion sites. It stores this data into a research repository designed to help better understand Tor’s hidden service ecosystem. The analysis component of our framework is called Automated Tool for Onion Labeling (ATOL), which introduces a two-stage thematic labeling strategy: (1) it learns descriptive and discriminative keywords for different categories, and (2) uses these terms to map onion site content to a set of thematic labels. We also present empirical results of ATOL and our ongoing experimentation with it, as we have gained experience applying it to the entirety of our darkweb repository, now over 70 million indexed pages. We find that ATOL can perform site-level thematic label assignment more accurately than keywordbased schemes developed by domain experts — we expand the analyst-provided keywords using an automatic keyword discovery algorithm, and get 12% gain in accuracy by using a machine learning classification model. We also show how ATOL can discover categories on previously unlabeled onions and discuss applications of ATOL in supporting various analyses and investigations of the darkweb.

Algorithms for equilibrium computation generally make no attempt to ensure that the computed strategies are understandable by humans. For instance the strategies for the strongest poker agents are represented as massive binary files. In many situations, we would like to compute strategies that can actually be implemented by humans, who may have computational limitations and may only be able to remember a small number of features or components of the strategies that have been computed. We study poker games where private information distributions can be arbitrary. We create a large training set of game instances and solutions, by randomly selecting the private information probabilities, and present algorithms that learn from the training instances in order to perform well in games with unseen information distributions. One approach first clusters the training points into a small number of clusters and then creates a small decision tree based on the cluster centers. This approach produces low test error and could be easily implemented by humans since it only requires memorizing a small number of "if-then" rules.

Learning approximate payoff functions is important to understand the dynamics in multi-player interactions. In general repeat games, each player's payoff can be represented as a combination of all other players' action choices using normal forms, which grow exponentially as the number of action choices increases. Graphical games, however, provide a compact representation to specify the inter-relations where one player's action choice is influenced by its neighbourhood. In this paper, we present how to learn players' approximate payoff functions from normal-form representations, yet also learn a compact graphical game representation of the inter-relations among the players. In this normal form representation, we explore the structural connections of mutual influence between players' action choices in game playing. We formally describe the problem of learning a player influence network and give a novel reward structure-learning algorithm for multiagent graphical games, called the Multi-Descendent Regression Learning Structure Algorithm (MDRLSA). We evaluate MDRLSA on random graphical games generated in GAMUT. Experiments show that MDRLSA can efficiently identify the independence among players and extract the influence graph accurately. The running time of MDRLSA increases linearly with the number of strategy profiles of a game. Compared with state-of-the-art graphical game model learning methods, MDRLSA shows efficiency in terms of time and accuracy.

Procedural content generation (PCG) for videogames relies on a commitment to the semantics of the game. Concepts such as enemies or solidity are required for the creation of levels for platformer games. As humans, we can instantly identify the underlying semantics of a game from brief snippets of game play video or from playing the game. Previous PCG systems have needed humans to identify the semantic properties of objects in the game, either implicitly or explicitly. We propose a system that can automatically learn the semantic properties of game objects by observation of events in the game via a causal learning framework. We apply this learning approach to play traces from the Super Mario Bros. series.

Domain adaptation deals with adapting classifiers trained on data from a source distribution, to work effectively on data from a target distribution. In this paper, we introduce the Nonlinear Embedding Transform (NET) for unsupervised domain adaptation. The NET reduces cross-domain disparity through nonlinear domain alignment. It also embeds the domain-aligned data such that similar data points are clustered together. This results in enhanced classification. To determine the parameters in the NET model (and in other unsupervised domain adaptation models), we introduce a validation procedure by sampling source data points that are similar in distribution to the target data. We test the NET and the validation procedure using popular image datasets and compare the classification results across competitive procedures for unsupervised domain adaptation.

Predictive analytics have proved promising capabilities and opportunities to many aspects of healthcare practice. Data-driven insights can provide an important part of the solution for curbing rising costs and improving care quality. The paper implements machine learning techniques in an attempt to support decision making in relation to elderly healthcare in Ireland, with a particular focus on hip fracture care. We adopt a combination of unsupervised and supervised learning for predicting patient outcomes. Initially, elderly patients are grouped based on the similarity of age, length of stay (LOS) and elapsed time to surgery. Using the K-Means algorithm, our clustering experiments suggest the presence of three coherent clusters of patients. Subsequently, the discovered clusters are utilised to train prediction models that address a particular cluster of patients individually. In particular, two machine learning models are trained for every cluster of patients in order to predict the inpatient LOS, and discharge destination. The developed models are claimed to make predictions with relatively high accuracy. Furthermore, the potential usefulness of the clustering-guided approach of prediction is discussed in general.

As datasets for machine learning grow larger, parallelization strategies become more and more important. Recent approaches to distributed modelfitting rely heavily either on consensus ADMM, where each node solves smallsub-problems using only local data, or on stochastic gradient methods thatdon't scale well to large numbers of cores in a cluster setting. For this reason, GPU clusters have become common prerequisites to large-scale machinelearning. This paper describes an unconventional training method that uses alternating direction methods and Bregman iteration to train a variety of machine learning models on CPUs while avoiding the drawbacks of consensus methods and without gradient descent steps. Using transpose reduction strategies, the proposed method reduces the optimization problems to a sequence of minimization sub-steps that can each be solved globally in closed form. The method provides strong scaling in the distributed setting, yielding linear speedups even when split over thousands of cores.

We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable. Our approach is motivated by a model of seismic events and signals, where events detected in distant regions are approximately independent given those in intermediate regions. We perform parallel inference by coloring a factor graph defined over regions of latent space, rather than individual model variables. Evaluating on a model of seismic event detection, we achieve significant speedups over serial MCMC with no degradation in inference quality.

In this paper, we study inexact damped Newton method implemented in a distributed environment. We are motivated by the original DiSCO algorithm [Communication-Efficient Distributed Optimization of Self-Concordant Empirical Loss, Yuchen Zhang and Lin Xiao, 2015].We show that this algorithm may not scale well and propose algorithmic modifications which lead to fewer communications and better load-balancing between nodes. Those modifications lead to a more efficient algorithm with better scaling. This was made possibly by introducing our new pre-conditioner which is specially designed so that the preconditioning step can be solved exactly and efficiently.Numerical experiments for minimization of regularized empirical loss with a 273GB instance shows the efficiency of proposed algorithm.