Soft-max boosting

International audienceThe standard multi-class classification risk, based on the binary loss, is rarely directly minimized. This is due to (i) the lack of convexity and (ii) the lack of smoothness (and even continuity). The classic approach consists in minimizing instead a convex surrogate. In this paper, we propose to replace the usually considered deterministic decision rule by a stochastic one, which allows obtaining a smooth risk (generalizing the expected binary loss, and more generally the cost-sensitive loss). Practically, this (empirical) risk is minimized by performing a gradient descent in the function space linearly spanned by a base learner (a.k.a. boosting). We provide a convergence analysis of the resulting algorithm and experiment it on a bunch of synthetic and real-world data sets (with noiseless and noisy domains, compared to convex and non-convex boosters)

Time-Dependent Synchrotron and Compton Spectra from Jets of Microquasars

Jet models for the high-energy emission of Galactic X-ray binary sources have regained significant interest with detailed spectral and timing studies of the X-ray emission from microquasars, the recent detection by the HESS collaboration of very-high-energy gamma-rays from the microquasar LS~5039, and the earlier suggestion of jet models for ultraluminous X-ray sources observed in many nearby galaxies. Here we study the synchrotron and Compton signatures of time-dependent electron injection and acceleration, adiabatic and radiative cooling, and different jet geometries in the jets of Galactic microquasars. Synchrotron, synchrotron-self-Compton, and external-Compton radiation processes with soft photons provided by the companion star and the accretion disk are treated. An analytical solution is presented to the electron kinetic equation for general power-law geometries of the jets for Compton scattering in the Thomson regime. We pay particular attention to predictions concerning the rapid flux and spectral variability signatures expected in a variety of scenarios, making specific predictions concerning possible spectral hysteresis, similar to what has been observed in several TeV blazars. Such predictions should be testable with dedicated monitoring observations of Galactic microquasars and ultraluminous X-ray sources using Chandra and/or XMM-Newton.Comment: Accepted for publication in ApJ; 37 manuscript pages, including 10 eps figures; uses AASTeX macro

A Convex Relaxation for Weakly Supervised Classifiers

This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we propose a cost function based on a convex relaxation of the soft-max loss. We then propose an algorithm specifically designed to efficiently solve the corresponding semidefinite program (SDP). Empirically, our method compares favorably to standard ones on different datasets for multiple instance learning and semi-supervised learning as well as on clustering tasks.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012