In this paper, we present BENCH (BiclusteringdrivenENsemble of Classifiers), an algorithm toconstruct an ensemble of classifiers through concurrentfeature and data point selection guided byunsupervised knowledge obtained from biclustering.BENCH is designed for underdeterminedproblems. In our experiments, we use Bayesian BeliefNetwork (BBN) classifiers as base classifiers inthe ensemble; however, BENCH can be applied toother classification models as well. We show thatBENCH is able to increase prediction accuracy ofa single classifier and traditional ensemble of classifiersby up to 15% on three microarray datasetsusing various weighting schemes for combining individualpredictions in the ensemble.

Lifted message passing algorithms exploit repeated structure within a given graphical model to answer queries efficiently. Given evidence, they construct a lifted network of supernodes and superpotentials corresponding to sets of nodes and potentials that are indistinguishable given the evidence. Recently, efficient algorithms were presented for updating the structure of an existing lifted network with incremental changes to the evidence. In the inference stage, however, current algorithms need to construct a separate lifted network for each evidence case and run a modified message passing algorithm on each lifted network separately. Consequently, symmetries across the inference tasks are not exploited. In this paper, we present a novel lifted message passing technique that exploits symmetries across multiple evidence cases. The benefits of this multi-evidence lifted inference are shown for several important AI tasks such as computing personalized PageRanks and Kalman filters via multi-evidence lifted Gaussian belief propagation.

Consider a video surveillance application that monitors some location. The application knows a set of activity models (that are either normal or abnormal or both), but in addition, the application wants to find video segments that are unexplained by any of the known activity models — these unexplained video segments may correspond to activities for which no previous activity model existed. In this paper, we formally define what it means for a given video segment to be unexplained (totally or partially) w.r.t. a given set of activity models and a probability threshold. We develop two algorithms – FindTUA and FindPUA – to identify Totally and Partially Unexplained Activities respectively, and show that both algorithms use important pruning methods. We report on experiments with a prototype implementation showing that the algorithms both run efficiently and are accurate.

In many of the possible applications as well as the theoretical models of computational social choice,the agents’ preferences are represented as partialorders. In this paper, we extend the maximum likelihood approach for defining “optimal” voting rules to this setting. We consider distributions in which the pairwise comparisons / incomparabilities between alternatives are drawn i.i.d. We call suchmodels pairwise-independentmodels and show that they correspond to a class of voting rules that we call pairwise scoring rules. This generalizes rulessuch as Kemeny and Borda. Moreover, we show that Borda is the only pairwise scoring rule that satisfies neutrality, when the outcome space is the set of all alternatives. We then study which voting rules defined for linear orders can be extended to partial orders via our MLE model. We show that any weakly neutral outcome scoring rule (includingany ranking/candidate scoring rule) based onthe weighted majority graph can be represented as the MLE of a weakly neutral pairwise-independent model. Therefore, all such rules admit natural extensionsto profiles of partial orders. Finally, we propose a specific MLE model π k for generating a set of k winning alternatives, and study the computational complexity of winner determination for the MLE of π k .

Designing dialog policies for voice-enabled interfaces is a tailoring job that is most often left to natural language processing experts. This job is generally redone for every new dialog task because cross-domain transfer is not possible. For this reason, machine learning methods for dialog policy optimization have been investigated during the last 15 years. Especially, reinforcement learning (RL) is now part of the state of the art in this domain. Standard RL methods require to test more or less random changes in the policy on users to assess them as improvements or degradations. This is called on policy learning. Nevertheless, it can result in system behaviors that are not acceptable by users. Learning algorithms should ideally infer an optimal strategy by observing interactions generated by a non-optimal but acceptable strategy, that is learning off-policy. In this contribution, a sample-efficient, online and off-policy reinforcement learning algorithm is proposed to learn an optimal policy from few hundreds of dialogues generated with a very simple handcrafted policy.

Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor readings from around the house. There are many reasons to recognise human behaviours; one being to monitor the elderly or cognitively impaired and detect potentially dangerous behaviours. We view the behaviour recognition problem as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the development of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. However, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exemplar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an approximation to this mapping, beginning with separate investigations on current methods proposed in the literature, identifying useful sensory outputs for behaviour recognition, and concluding by proposing two directions: one using supervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.

Distributed electricity producers, such as small wind farms and solar installations, pose several technical and economic challenges in Smart Grid design. One approach to addressing these challenges is through Broker Agents who buy electricity from distributed producers, and also sell electricity to consumers, via a Tariff Market--a new market mechanism where Broker Agents publish concurrent bid and ask prices. We investigate the learning of pricing strategies for an autonomous Broker Agent to profitably participate in a Tariff Market. We employ Markov Decision Processes (MDPs) and reinforcement learning. An important concern with this method is that even simple representations of the problem domain result in very large numbers of states in the MDP formulation because market prices can take nearly arbitrary real values. In this paper, we present the use of derived state space features, computed using statistics on Tariff Market prices and Broker Agent customer portfolios, to obtain a scalable state representation. We also contribute a set of pricing tactics that form building blocks in the learned Broker Agent strategy. We further present a Tariff Market simulation model based on real-world data and anticipated market dynamics. We use this model to obtain experimental results that show the learned strategy performing vastly better than a random strategy and significantly better than two other non-learning strategies.

The data at our disposal consists of onboard sensor measurements that have been collected from truck round-trips. We advocate the use of an explicit time representation By applying a simple discretization method, we obtain sequences in syntactic pattern recognition because it can of timed events. The behavior that is displayed in result in more succinct models and easier learning these sequences is unknown. From this data, we want to learn problems. We apply this approach to the real-world a model that we can use to monitor the driving behavior in problem of learning models for the driving behavior new data, i.e., to use it as a classifier. Our approach is to first of truck drivers. We discretize the values of learn a timed model from the unlabeled sequences using the onboard sensors into simple events.

We propose a general MCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generative models including Bayesian networks and PCFGs. The idea is to generalize an MCMC method for PCFGs to the one for a Turing-complete probabilistic modeling language PRISM in the context of statistical abduction where parse trees are replaced with explanations. We describe how to estimate the marginal probability of data from MCMC samples and how to perform Bayesian Viterbi inference using an example of Naive Bayes model augmented with a hidden variable.

In this paper, we describe how we integrated an artificial intelligence (AI) system into the PubMed search website using augmented browsing technology. Our system dynamically enriches the PubMed search results displayed in a user’s browser with semantic annotation provided by several natural language processing (NLP) subsystems, including a sentence splitter, a part-of-speech tagger, a named entity recognizer, a section categorizer and a gene normalizer (GN). After our system is installed, the PubMed search results page is modified on the fly to categorize sections and provide additional information on gene and gene products identified by our NLP subsystems. In addition, GN involves three main steps: candidate ID matching, false positive filtering and disambiguation, which are highly dependent on each other. We propose a joint model using a Markov logic network (MLN) to model the dependencies found in GN. The experimental results show that our joint model outperforms a baseline system that executes the three steps separately. The developed system is available at https://sites.google.com/site/pubmedannotationtool4ijcai/home.