Structural Sampling for Statistical Software Testing

Structural Statistical Software Testing exploits the control flow graph of the program being tested to construct test cases. While test cases can easily be extracted from {em feasible paths} in the control flow graph, that is, paths which are actually exerted for some values of the program input, the feasible path region is a tiny fraction of the graph paths (less than $10^{-5}]$ for medium size programs). The S4T algorithm presented in this paper aims to address this limitation; as an Active Relational Learning Algorithm, it uses the few feasible paths initially available to sample new feasible paths. The difficulty comes from the non-Markovian nature of the feasible path concept, due to the long-range dependencies between the nodes in the control flow graph. Experimental validation on real-world and artificial problems demonstrates significant improvements compared to the state of the art

Toward Optimal Run Racing: Application to Deep Learning Calibration

This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand. The notoriously expensive calibration problem is optimally reduced by detecting and early stopping non-optimal runs. The theoretical contribution regards the optimality guarantees within the multiple hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki benchmarks demonstrate the relevance of the approach with a principled and consistent improvement on the state of the art with no extra hyper-parameter

Comparison-Based Optimizers Need Comparison-Based Surrogates

Taking inspiration from approximate ranking, this paper nvestigates the use of rank-based Support Vector Machine as surrogate model within CMA-ES, enforcing the invariance of the approach with respect to monotonous transformations of the fitness function. Whereas the choice of the SVM kernel is known to be a critical issue, the proposed approach uses the Covariance Matrix adapted by CMA-ES within a Gaussian kernel, ensuring the adaptation of the kernel to the currently explored region of the fitness landscape at almost no computational overhead. The empirical validation of the approach on standard benchmarks, comparatively to CMA-ES and recent surrogate-based CMA-ES, demonstrates the efficiency and scalability of the proposed approach

Not all parents are equal for MO-CMA-ES

The Steady State variants of the Multi-Objective Covariance Matrix Adaptation Evolution Strategy (SS-MO-CMA-ES) generate one offspring from a uniformly selected parent. Some other parental selection operators for SS-MO-CMA-ES are investigated in this paper. These operators involve the definition of multi-objective rewards, estimating the expectation of the offspring survival and its Hypervolume contribution. Two selection modes, respectively using tournament, and inspired from the Multi-Armed Bandit framework, are used on top of these rewards. Extensive experimental validation comparatively demonstrates the merits of these new selection operators on unimodal MO problems

Dominance-Based Pareto-Surrogate for Multi-Objective Optimization

Mainstream surrogate approaches for multi-objective problems build one approximation for each objective. Mono-surrogate approaches instead aim at characterizing the Pareto front with a single model. Such an approach has been recently introduced using a mixture of regression Support Vector Machine (SVM) to clamp the current Pareto front to a single value, and one-class SVM to ensure that all dominated points will be mapped on one side of this value. A new mono-surrogate EMO approach is introduced here, relaxing the previous approach and modelling Pareto dominance within the rank-SVM framework. The resulting surrogate model is then used as a filter for offspring generation in standard Evolutionary Multi-Objective Algorithms, and is comparatively validated on a set of benchmark problems

Adaptive Coordinate Descent

Independence from the coordinate system is one source of efficiency and robustness for the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The recently proposed Adaptive Encoding (AE) procedure generalizes CMA-ES adaptive mechanism, and can be used together with any optimization algorithm. Adaptive Encoding gradually builds a transformation of the coordinate system such that the new coordinates are as decorrelated as possible with respect to the objective function. But any optimization algorithm can then be used together with Adaptive Encoding, and this paper proposes to use one of the simplest of all, that uses a dichotomy procedure on each coordinate in turn. The resulting algorithm, termed Adaptive Coordinate Descent (ACiD), is analyzed on the Sphere function, and experimentally validated on BBOB testbench where it is shown to outperform the standard (1 + 1)-CMA-ES, and is found comparable to other state-of-the-art CMA-ES variants

A Pareto-Compliant Surrogate Approach for Multiobjective Optimization

This paper discusses the idea of using a single Pareto-compliant surrogate model for multiobjective optimization. While most surrogate approaches to multi-objective optimization build a surrogate model for each objective, the recently proposed mono surrogate approach aims at building a global surrogate model defined on the decision space and tightly characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward the true Pareto set. This surrogate model is specified by combining a One-class Support Vector Machine (SVMs) to characterize the dominated points, and a Regression SVM to clamp the Pareto front on a single value. The aims of this paper are to identify issues of the proposed approach demanding further study and to raise the question of how to efficiently incorporate quality indicators, such as the hypervolume into the surrogate model

Macular Microcysts in Mitochondrial Optic Neuropathies: Prevalence and Retinal Layer Thickness Measurements.

PurposeTo investigate the thickness of the retinal layers and to assess the prevalence of macular microcysts (MM) in the inner nuclear layer (INL) of patients with mitochondrial optic neuropathies (MON).MethodsAll patients with molecularly confirmed MON, i.e. Leber's Hereditary Optic Neuropathy (LHON) and Dominant Optic Atrophy (DOA), referred between 2010 and 2012 were enrolled. Eight patients with MM were compared with two control groups: MON patients without MM matched by age, peripapillary retinal nerve fiber layer (RNFL) thickness, and visual acuity, as well as age-matched controls. Retinal segmentation was performed using specific Optical coherence tomography (OCT) software (Carl Zeiss Meditec). Macular segmentation thickness values of the three groups were compared by one-way analysis of variance with Bonferroni post hoc corrections.ResultsMM were identified in 5/90 (5.6%) patients with LHON and 3/58 (5.2%) with DOA. The INL was thicker in patients with MON compared to controls regardless of the presence of MM [133.1±7μm vs 122.3±9μm in MM patients (p<0.01) and 128.5±8μm vs. 122.3±9μm in no-MM patients (p<0.05)], however the outer nuclear layer (ONL) was thicker in patients with MM (101.4±1mμ) compared to patients without MM [77.5±8mμ (p<0.001)] and controls [78.4±7mμ (p<0.001)]. ONL thickness did not significantly differ between patients without MM and controls.ConclusionThe prevalence of MM in MON is low (5-6%), but associated with ONL thickening. We speculate that in MON patients with MM, vitreo-retinal traction contributes to the thickening of ONL as well as to the production of cystic spaces

Congestion-Avoiding Job Recommendation with Optimal Transport

International audienceThe matching setting, a particular case of recommendation problem, focuses on applications where a so-called item can be attributed to at most one user, with the job market and the matrimonial market as chief examples. In such cases, recommending the items preferred by each user might contribute to a congestion issue as users aiming at the same item cannot be all satisfied.While some state of art approaches proceed by repairing the recommendation policy to account for the congestion issue, other approaches take inspiration from the optimal transport (OT) framework, and aim to map the user population onto the item population in order to minimize some global transportation cost. In OT-based recommendation state-of-art approaches, the collaborative matrix (reporting the user-item matches) is interpreted as if it were the result of an OT plan; the underlying transport cost is inferred and used e.g. to propose new congestion-avoiding recommendation policies.In this paper, another OT-based recommendation strategy is defined, noting that the collaborative matrix reflecting the individual decisions can hardly be considered as the result of a (centralized) OT plan. Accordingly, the proposed algorithm first learns a recommendation score from the data and then defines a cost matrix, with the transportation cost decreasing depending on the matching relevance. The optimal transport plan is thereafter used for the recommendation.The experimental validation of the approach presents comparative results on proprietary real-world data on the job market, illustrating the trade-off between the recall and the congestion indicators