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Protecting Mobile Food Diaries from Getting too Personal

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Applications of Approximate Learning and Inference for Probabilistic Models

Young Jun Ko

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Learning Explainable User Sentiment and Preferences for Information Filtering

Nikolaos Pappas

In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...

EPFL2016

Principled Parallel Mean-Field Inference for Discrete Random Fields

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Timur Bagautdinov

Mean-field variational inference is one of the most popular approaches to inference in discrete random fields. Standard mean-field optimization is based on coordinate descent and in many situations can be impractical. Thus, in practice, various parallel te ...

2016

Kullback-Leibler Proximal Variational Inference

Pascal Fua, François Fleuret, Pierre Bruno Baqué, Mohammad Emtiyaz Khan

We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and structure of the variational bound. Firstly, we p ...

2015

Statistics of Extremes

Anthony Christopher Davison, Raphaël Huser

Statistics of extremes concerns inference for rare events. Often the events have never yet been observed, and their probabilities must therefore be estimated by extrapolation of tail models fitted to available data. Because data concerning the event of int ...

Annual Reviews2015

Tractable Approaches to Learning and Planning in High Dimensions

Leonidas Lefakis

The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...

EPFL2014

Principal Polynomial Analysis

Devis Tuia

This paper presents a new framework for manifold learning based on a sequence of principal polynomials that capture the possibly nonlinear nature of the data. The proposed Principal Polynomial Analysis (PPA) generalizes PCA by modeling the directions of ma ...

World Scientific Publ Co Pte Ltd2014

Quantifying and Protecting Location Privacy

Reza Shokri

Recent developments in information and communication technologies have been profound and life-changing. Most people are now equipped with smart phones with high computation power and communication capabilities. These devices can efficiently run multiple so ...

EPFL2013

Mitigating Anonymity Challenges in Automated Testing and Debugging Systems

George Candea, Silviu Andrica

Modern software often provides automated testing and bug reporting facilities that enable developers to improve the software after release. Alas, this comes at the cost of user anonymity: reported execution traces may identify users. We present a way to mi ...

2013

Quantifying and Protecting Location Privacy

Reza Shokri

EPFL2012