Publications related to Empirical risk minimization

Importance Sampling Tree for Large-scale Empirical Expectation

François Fleuret, Olivier Canévet, Cijo Jose

We propose a tree-based procedure inspired by the Monte-Carlo Tree Search that dynamically modulates an importance-based sampling to prioritize computation, while getting unbiased estimates of weighted sums. We apply this generic method to learning on very ...

2016

Robustness of classifiers: from adversarial to random noise

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to ran ...

2016

Path-following gradient-based decomposition algorithms for separable convex optimization

Quoc Tran Dinh

A new decomposition optimization algorithm, called path-following gradient-based decomposition, is proposed to solve separable convex optimization problems. Unlike path-following Newton methods considered in the literature, this algorithm does not require ...

Springer Verlag2014

Managing Evolving Uncertainty in Trajectory Databases

Saket Sathe, Ho Young Jeung

Modern positioning technologies enable collecting trajectories from moving objects across different locations over time, typically containing time-varying measurement errors of positioning systems. Unfortunately, current models on uncertain trajectories ar ...

Ieee Computer Soc2014

Online dictionary learning over distributed models

Ali H. Sayed

In this paper, we consider learning dictionary models over a network of agents, where each agent is only in charge of a portion of the dictionary elements. This formulation is relevant in big data scenarios where multiple large dictionary models may be spr ...

IEEE2014

treeKL: A distance between high dimension empirical distributions

François Fleuret

This paper offers a methodological contribution for computing the distance between two empirical distributions in an Euclidean space of very large dimension. We propose to use decision trees instead of relying on standard quantification of the feature spac ...

Elsevier Science Bv2013

Learning User's Confidence for Active Learning

Devis Tuia

In this paper, we study the applicability of active learning (AL) in operative scenarios. More particularly, we consider the well-known contradiction between the AL heuristics, which rank the pixels according to their uncertainty, and the user's confidence ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Generalized Total Variation Denoising Via Augmented Lagrangian Cycle Spinning With Haar Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We consider the denoising of signals and images using regularized least-squares method. In particular, we propose a simple minimization algorithm for regularizers that are functions of the discrete gradient. By exploiting the connection of the discrete gra ...

Ieee2012

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We consider the denoising of signals and images using regularized least-squares method. In particular, we propose a simple minimization algorithm for regularizers that are functions of the discrete gradient. By exploiting the connection of the discrete gr ...

IEEE2012

OM-2: An Online Multi-class Multi-kernel Learning Algorithm

Barbara Caputo, Jie Luo, Marco Fornoni

Efficient learning from massive amounts of information is a hot topic in computer vision. Available training sets contain many examples with several visual descriptors, a setting in which current batch approaches are typically slow and does not scale well. ...

Idiap2010

Empirical risk minimization

Importance Sampling Tree for Large-scale Empirical Expectation

Robustness of classifiers: from adversarial to random noise

Path-following gradient-based decomposition algorithms for separable convex optimization

Managing Evolving Uncertainty in Trajectory Databases

Online dictionary learning over distributed models

treeKL: A distance between high dimension empirical distributions

Learning User's Confidence for Active Learning

Generalized Total Variation Denoising Via Augmented Lagrangian Cycle Spinning With Haar Wavelets

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

OM-2: An Online Multi-class Multi-kernel Learning Algorithm

Graph Chatbot

Chat with Graph Search

Managing Evolving Uncertainty in Trajectory Databases

Path-following gradient-based decomposition algorithms for separable convex optimization

Robustness of classifiers: from adversarial to random noise

Online dictionary learning over distributed models

Importance Sampling Tree for Large-scale Empirical Expectation

Learning User's Confidence for Active Learning

treeKL: A distance between high dimension empirical distributions

Generalized Total Variation Denoising via Augmented Lagrangian Cycle Spinning with Haar Wavelets

Generalized Total Variation Denoising Via Augmented Lagrangian Cycle Spinning With Haar Wavelets

OM-2: An Online Multi-class Multi-kernel Learning Algorithm