Minimax Classification with 0-1 Loss and Performance Guarantees

Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. MRCs achieve efficient learning and out-of-sample generalization by minimizing worst-case expected 0-1 loss w.r.t. uncertainty sets that are defined by linear constraints and include the true underlying distribution. In addition, MRCs’ learning stage provides performance guarantees as lower and upper tight bounds for expected 0-1 loss. We also present MRCs’ finite-sample generalization bounds in terms of training size and smallest minimax risk, and show their competitive classification performance w.r.t. state-of-the-art techniques using benchmark datasets.Ramon y Cajal Grant RYC-2016-1938

A method of moments estimator for interacting particle systems and their mean field limit

We study the problem of learning unknown parameters in stochastic interacting particle systems with polynomial drift, interaction and diffusion functions from the path of one single particle in the system. Our estimator is obtained by solving a linear system which is constructed by imposing appropriate conditions on the moments of the invariant distribution of the mean field limit and on the quadratic variation of the process. Our approach is easy to implement as it only requires the approximation of the moments via the ergodic theorem and the solution of a low-dimensional linear system. Moreover, we prove that our estimator is asymptotically unbiased in the limits of infinite data and infinite number of particles (mean field limit). In addition, we present several numerical experiments that validate the theoretical analysis and show the effectiveness of our methodology to accurately infer parameters in systems of interacting particles

A Numerical Study of Vibration-Induced Instrument Reading Capability Degradation in Helicopter Pilots

Rotorcraft suffer from relatively high vibratory levels, due to exposure to significant vibratory load levels originating from rotors. As a result, pilots are typically exposed to vibrations, which have non-negligible consequences. Among those, one important issue is the degradation of instrument reading, which is a result of complex human-machine interaction. Both involuntary acceleration of the eyes as a result of biodynamics and vibration of the instrument panel contribute to a likely reduction in instrument reading capability, affecting flight safety. Therefore, being able to estimate the expected level of degradation in visual performance may give substantial benefits during vehicle design, allowing to make necessary adjustments while there is room for design changes or when retrofitting an existing aircraft to ensure the modifications do not adversely affect visual acuity and instrument reading ability. For this purpose, simulation is a very valuable tool as a proper model helps to understand the aircraft characteristics before conducting flight tests. This work presents the assessment of vibration-induced visual degradation of helicopter pilots under vibration exposure using a modular analysis environment. Core elements of the suggested analysis framework are an aeroelastic model of the helicopter, a model of the seat-cushion subsystem, a detailed multibody model of the human biodynamics, and a simplified model of ocular dynamics. These elements are combined into a comprehensive, fully coupled model. The contribution of each element to instrument reading degradation is examined, after defining an appropriate figure of merit that includes both eye and instrument panel vibration, in application to a numerical model representative of a medium-weight helicopter

Searching for dominant high-level features for music information retrieval

Music Information Retrieval systems are often based on the analysis of a large number of low-level audio features. When dealing with problems of musical genre description and visualization, however, it would be desirable to work with a very limited number of highly informative and discriminant macro-descriptors. In this paper we focus on a specific class of training-based descriptors, which are obtained as the loglikelihood of a Gaussian Mixture Model trained with short musical excerpts that selectively exhibit a certain semantic homogeneity. As these descriptors are critically dependent on the training sets, we approach the problem of how to automatically generate suitable training sets and optimize the associated macro-features in terms of discriminant power and informative impact. We then show the application of a set of three identified macro-features to genre visualization, tracking and classification