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We propose a machine learning approach to action prediction in one-shot games. In contrast to the huge literature on learning in games where an agent’s model is deduced from its previous actions in a multi-stage game, we propose the idea of inferring correlations between agents’ actions in different one-shot games in order to predict an agent’s action in a game which she did not play yet. We define...
We propose a novel active learning strategy based on the compression framework of [9] for label ranking functions which, given an input instance, predict a total order over a predefined set of alternatives. Our approach is theoretically motivated by an extension to ranking and active learning of Kääriäinen’s generalization bounds using unlabeled data [7], initially developed in the context of classification...
A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comrafs in...
Trees provide a suited structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, or conversion of tree structured documents. In this context, many applications require the calculation of similarities between tree pairs. The most studied distance is likely the tree edit distance (ED) for which improvements in terms of complexity have...
We are interested in using Inductive Logic Programming (ILP) to infer grammars representing sets of protein sequences. ILP takes as input both examples and background knowledge predicates. This work is a first step in optimising the choice of background knowledge predicates for predicting the function of proteins. We propose methods to obtain different sets of background knowledge. We then study the...
Bayesian network structure identification is known to be NP-Hard in the general case. We demonstrate a heuristic search for structure identification based on aggregationhierarchies. The basic idea is to perform initial exhaustive searches on composite “high-level” random variables (RVs) that are created via aggregations of atomic RVs. The results of the high-level searches then constrain...
We propose in this paper a novel approach to the classification of discrete sequences. This approach builds a model fitting some dynamical features deduced from the learning sample. These features are discrete phase-type (PH) distributions. They model the first passage times (FPT) between occurrences of pairs of substrings. The PHit algorithm, an adapted version of the Expectation-Maximization algorithm,...
Using string kernels, languages can be represented as hyperplanes in a high dimensional feature space. We present a new family of grammatical inference algorithms based on this idea. We demonstrate that some mildly context sensitive languages can be represented in this way and it is possible to efficiently learn these using kernel PCA. We present some experiments demonstrating the effectiveness of...
Over the last twenty years AI has undergone a sea change. The once-dominant paradigm of logical inference over symbolic knowledge representations has largely been supplanted by statistical methods. The statistical paradigm affords a robustness in the real-world that has eluded symbolic logic. But statistics sacrifices much in expressiveness and inferential richness, which is achieved by first-order...
Combining statistical and relational learning receives currently a lot of attention. The majority of statistical relational learning approaches focus on density estimation. For classification, however, it is well-known that the performance of such generative models is often lower than that of discriminative classifiers. One approach to improve the performance of generative models is to combine them...
Evaluating classifier performance with ROC curves is popular in the machine learning community. To date, the only method to assess confidence of ROC curves is to construct ROC bands. In the case of severe class imbalance with few instances of the minority class, ROC bands become unreliable. We propose a generic framework for classifier evaluation to identify a segment of an ROC curve in which misclassifications...
Automatically acquiring control-knowledge for planning, as it is the case for Machine Learning in general, strongly depends on the training examples. In the case of planning, examples are usually extracted from the search tree generated when solving problems. Therefore, examples depend on the problems used for training. Traditionally, these problems are randomly generated by selecting some difficulty...
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical applications (e.g. speech recognition, biological sequence alignment) it has two major limitations: it requires a known model topology, and learning is only locally optimal. We propose a new PAC framework for learning both...
This work proposes a new approach to the retrieval of images from text queries. Contrasting with previous work, this method relies on a discriminative model: the parameters are selected in order to minimize a loss related to the ranking performance of the model, i.e. its ability to rank the relevant pictures above the non-relevant ones when given a text query. In order to minimize this loss, we introduce...
Conditional Random Fields (CRFs) provide a powerful instrument for labeling sequences. So far, however, CRFs have only been considered for labeling sequences over flat alphabets. In this paper, we describe TildeCRF, the first method for training CRFs on logical sequences, i.e., sequences over an alphabet of logical atoms. TildeCRF’s key idea is to use relational regression trees in Dietterich et al...
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available. We propose an original reformulation of the MIL concept for the unsupervised context (UMIL), which can serve as a broader framework for clustering data objects adequately described by the multiple-instance representation...
We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naïve Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar complexity. Our Markov network model is represented as a set of consistent probability distributions on subsets of variables. Inference with such a model...
Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for the closed-loop learning of mappings from images to actions. This approach requires a family of function approximators that maps visual percepts to a real-valued function. For this purpose, we use Regression Extra-Trees,...
We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJC), adaptively discretizes the joint space of visual percepts and continuous actions. In a sequence of attempts to remove perceptual aliasing, it incrementally builds a decision tree that applies tests either in the input...
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this paper, we present the EM algorithm to learn the parameters in causal independence models based on the symmetric Boolean function. The developed algorithm enables us to assess the practical usefulness of the symmetric causal...
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