A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

International audienceWe present a novel hybrid algorithm for Bayesian network structure learning, called Hybrid HPC (H2PC). It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. It is based on a subroutine called HPC, that combines ideas from incremental and divide-and-conquer constraint-based methods to learn the parents and children of a target variable. We conduct an experimental comparison of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning, on several benchmarks with various data sizes. Our extensive experiments show that H2PC outperforms MMHC both in terms of goodness of fit to new data and in terms of the quality of the network structure itself, which is closer to the true dependence structure of the data. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available

Causal Confusion in Imitation Learning

Behavioral cloning reduces policy learning to supervised learning by training a discriminative model to predict expert actions given observations. Such discriminative models are non-causal: the training procedure is unaware of the causal structure of the interaction between the expert and the environment. We point out that ignoring causality is particularly damaging because of the distributional shift in imitation learning. In particular, it leads to a counter-intuitive "causal misidentification" phenomenon: access to more information can yield worse performance. We investigate how this problem arises, and propose a solution to combat it through targeted interventions---either environment interaction or expert queries---to determine the correct causal model. We show that causal misidentification occurs in several benchmark control domains as well as realistic driving settings, and validate our solution against DAgger and other baselines and ablations.Comment: Published at NeurIPS 2019 9 pages, plus references and appendice

A phylogenomic perspective on diversity, hybridization and evolutionary affinities in the stickleback genus Pungitius

Hybridization and convergent evolution are phenomena of broad interest in evolutionary biology, but their occurrence poses challenges for reconstructing evolutionary affinities among affected taxa. Sticklebacks in the genus Pungitius are a case in point: evolutionary relationships and taxonomic validity of different species and populations in this circumpolarly distributed species complex remain contentious due to convergent evolution of traits regarded as diagnostic in their taxonomy, and possibly also due to frequent hybridization among taxa. To clarify the evolutionary relationships among different Pungitius species and populations globally, as well as to study the prevalence and extent of introgression among recognized species, genomic data sets of both reference genome-anchored single nucleotide polymorphisms and de novo assembled RAD-tag loci were constructed with RAD-seq data. Both data sets yielded topologically identical and well-supported species trees. Incongruence between nuclear and mitochondrial DNA-based trees was found and suggested possibly frequent hybridization and mitogenome capture during the evolution of Pungitius sticklebacks. Further analyses revealed evidence for frequent nuclear genetic introgression among Pungitius species, although the estimated proportions of autosomal introgression were low. Apart from providing evidence for frequent hybridization, the results challenge earlier mitochondrial and morphology-based hypotheses regarding the number of species and their affinities in this genus: at least seven extant species can be recognized on the basis of genetic data. The results also shed new light on the biogeographical history of the Pungitius-complex, including suggestion of several trans-Arctic invasions of Europe from the Northern Pacific. The well-resolved phylogeny should facilitate the utility of this genus as a model system for future comparative evolutionary studies.Peer reviewe