In this document we provide a complete derivation for multi-class boosting with group sparsity and a full explanation of admm algorithm presented in the main paper. 1 Multi-class boosting with group sparsity We first provide the derivation for multi-class logistic loss with 1,2-norm. We then show the difference between our boosting with 1,2-norm and 1,∞-norm. We then briefly discuss our group s...

Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. ...

Embeddings of knowledge graphs have received significant attention due to their excellent performance for tasks like link prediction and entity resolution. In this short paper, we are providing a comparison of two state-of-the-art knowledge graph embeddings for which their equivalence has recently been established, i.e., COMPLEX and HOLE [Nickel, Rosasco, and Poggio, 2016; Trouillon et al., 201...

This paper revisits the problem of learning a k-CNF Boolean function from examples, for fixed k, in the context of online learning under the logarithmic loss. We give a Bayesian interpretation to one of Valiant’s classic PAC learning algorithms, which we then build upon to derive three efficient, online, probabilistic, supervised learning algorithms for predicting the output of an unknown k-CNF...

Intonation modelling is an integral part of text-to-speech systems from their very beginnings. This has led to the proliferation of various intonation models, each with its own relative strengths and weaknesses. Only a few of these intonation models are based on physiology, despite the advantage that such models are language independent. We propose a new intonation model inspired by the physiol...

We present a new way to understand and characterize the choice of scoring, objective , or loss function for a model, machine, or expert estimating (i.e. predicting) the probabilities of events or outcomes (such as class membership). The ultimate value of a probability estimate lies in the actual payoo (utility) accruing to those who use this information to make a decision. We allow that we ofte...

Using a finite-element mode solver, the modes of a commercial endlessly single-mode photonic crystal fiber (ESM-PCF) were investigated. Based on the loss discrimination between the dominant and the nearest higher order mode, we set-up a criterion for the single-modeness. Using that measure, we verified the single-modeness of the corresponding ESM-PCF and found that the endlessly single-modeness...