Efficient Covariance Matrix Update for Variable Metric Evolution Strategies

International audienceRandomized direct search algorithms for continuous domains, such as Evolution Strategies, are basic tools in machine learning. They are especially needed when the gradient of an objective function (e.g., loss, energy, or reward function) cannot be computed or estimated efficiently. Application areas include supervised and reinforcement learning as well as model selection. These randomized search strategies often rely on normally distributed additive variations of candidate solutions. In order to efficiently search in non-separable and ill-conditioned landscapes the covariance matrix of the normal distribution must be adapted, amounting to a variable metric method. Consequently, Covariance Matrix Adaptation (CMA) is considered state-of-the-art in Evolution Strategies. In order to sample the normal distribution, the adapted covariance matrix needs to be decomposed, requiring in general $\Theta(n^3)$ operations, where $n$ is the search space dimension. We propose a new update mechanism which can replace a rank-one covariance matrix update and the computationally expensive decomposition of the covariance matrix. The newly developed update rule reduces the computational complexity of the rank-one covariance matrix adaptation to $\Theta(n^2)$ without resorting to outdated distributions. We derive new versions of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the multi-objective CMA-ES. These algorithms are equivalent to the original procedures except that the update step for the variable metric distribution scales better in the problem dimension. We also introduce a simplified variant of the non-elitist CMA-ES with the incremental covariance matrix update and investigate its performance. Apart from the reduced time-complexity of the distribution update, the algebraic computations involved in all new algorithms are simpler compared to the original versions. The new update rule improves the performance of the CMA-ES for large scale machine learning problems in which the objective function can be evaluated fast

The Novel Human Influenza A(H7N9) Virus Is Naturally Adapted to Efficient Growth in Human Lung Tissue

A novel influenza A virus (IAV) of the H7N9 subtype has been isolated from severely diseased patients with pneumonia and acute respiratory distress syndrome and, apparently, from healthy poultry in March 2013 in Eastern China. We evaluated replication, tropism, and cytokine induction of the A/Anhui/1/2013 (H7N9) virus isolated from a fatal human infection and two low-pathogenic avian H7 subtype viruses in a human lung organ culture system mimicking infection of the lower respiratory tract. The A(H7N9) patient isolate replicated similarly well as a seasonal IAV in explanted human lung tissue, whereas avian H7 subtype viruses propagated poorly. Interestingly, the avian H7 strains provoked a strong antiviral type I interferon (IFN-I) response, whereas the A(H7N9) virus induced only low IFN levels. Nevertheless, all viruses analyzed were detected predominantly in type II pneumocytes, indicating that the A(H7N9) virus does not differ in its cellular tropism from other avian or human influenza viruses. Tissue culture-based studies suggested that the low induction of the IFN-β promoter correlated with an efficient suppression by the viral NS1 protein. These findings demonstrate that the zoonotic A(H7N9) virus is unusually well adapted to efficient propagation in human alveolar tissue, which most likely contributes to the severity of lower respiratory tract disease seen in many patients

Optimierung von Fahrerassistenzsystemen

Diese Arbeit gliedert sich in einen methodischen und einen anwendungsorientierten Teil, wobei sich der methodische Teil mit der Verbesserung der zeitlichen Effizienz von Evolutionsstrategien (ESs) und Support-Vektor-Maschinen (SVMs) befasst. In diesem Teil wird die Berechnungskomplexität der Covariance-Matrix-Adaptation-ES (CMA-ES)auf das theoretische Minimum reduziert und ein neues Approximationsverfahren für SVMs entwickelt, das eine größere Leistungsfähigkeit als bestehende Techniken aufweist. Im anwendungsorientierten Teil werden Komponenten für Fahrerassistenzsysteme entwickelt und optimiert. Dabei spielen die im methodischen Teil entwickelten Verfahren eine zentrale Rolle. Mit der CMA-ES werden die optimalen Parameter eines Fahrspurfindungssystems bestimmt. Des Weiteren wird ein effizientes und robustes System zur Fluchtpunktschätzung entwickelt. Abschließend wird für die Fußgängerdetektion mit SVMs die Technik der evolutionären Mehrzieloptimierung vorgeschlagen und eingesetzt

In: Yaochu Jin (Ed.), Multi-objective Machine Learning

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