Relational Representations in Reinforcement Learning: Review and Open Problems

This paper is about representation in RL.We discuss some of the concepts in representation and generalization in reinforcement learning and argue for higher-order representations, instead of the commonly used propositional representations. The paper contains a small review of current reinforcement learning systems using higher-order representations, followed by a brief discussion. The paper ends with research directions and open problems.\u

KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter

Multi-label classification allows a datapoint to be labelled with more than one class at the same time. In spite of their success in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Experiments described in this article show that KFHE-HOMER performs consistently better than existing multi-label methods including existing approaches based on ensembles.Comment: The paper is under consideration at Pattern Recognition Letters, Elsevie

Interpretable Clustering using Unsupervised Binary Trees

We herein introduce a new method of interpretable clustering that uses unsupervised binary trees. It is a three-stage procedure, the first stage of which entails a series of recursive binary splits to reduce the heterogeneity of the data within the new subsamples. During the second stage (pruning), consideration is given to whether adjacent nodes can be aggregated. Finally, during the third stage (joining), similar clusters are joined together, even if they do not descend from the same node originally. Consistency results are obtained, and the procedure is used on simulated and real data sets.Comment: 25 pages, 6 figure

Inverse Bifurcation Problem as a Tool For Rapid Identification of Progressive Collapse for Thin-Walled Systems

Notwithstanding recent advances in robust design, the problem of vulnerability of structures is still open. On the one hand, this leads to various structure collapses; on the other hand, this prompts researchers to develop models and methods to identify a state o f progressive collapse and estimate lifetime and residual functionality of perturbed structure. An inverse bifurcation problem implies that one identifies a pre-bifurcation state of a perturbed thin-walled system. The topological precursor (a tool to solve an inverse bifurcation problem) used is based on typical sequences o f deformed states extracted from clustered post-critical solutions o f non-linear boundary problem o f thin-walled systems theory. It implies that complete bifurcation structure o f the non-linear boundary problem (including primary, secondary and tertiary bifurcation paths) are constructed. The proposed approach was employed to identify a pre-bifurcation state of a cylindrical shell under uniform pressure (close to the critical) subjected to a pulse impact

Modeling Moods in Violin Performances

(Abstract to follow

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global approach, which trains a single model over pairs of nodes. Here, we systematically investigate, theoretically and empirically, the exploitation of tree-based ensemble methods in the context of these two approaches for biological network inference. We first formalize the problem of network inference as classification of pairs, unifying in the process homogeneous and bipartite graphs and discussing two main sampling schemes. We then present the global and the local approaches, extending the later for the prediction of interactions between two unseen network nodes, and discuss their specializations to tree-based ensemble methods, highlighting their interpretability and drawing links with clustering techniques. Extensive computational experiments are carried out with these methods on various biological networks that clearly highlight that these methods are competitive with existing methods.Comment: 22 page

Random forests with random projections of the output space for high dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage