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Inferring activities from context measurements using Bayesian inference and random utility models

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Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

EPFL2016

Exploiting pedestrian WiFi traces for destination choice modeling

Loïc Tinguely

My master thesis proposes a general methodology to model pedestrian destination choice from WiFi localization in multimodal transport facilities (e.g., airports, railway stations). It is based on the output of [Danalet, A., B. Farooq and M. Bierlaire (2014 ...

2015

Combine and Conquer

Vincent Etter

In this thesis, we explore the application of data mining and machine learning techniques to several practical problems. These problems have roots in various fields such as social science, economics, and political science. We show that computer science tec ...

EPFL2015

Location Choice With Longitudinal Wifi Data: Research Data

Michel Bierlaire, Matthieu Marie Cochon de Lapparent, Antonin Danalet, Loïc Tinguely

This document describes the softwares, data, models and raw results associated to: Danalet, A., Tinguely, L., Lapparent, M. d. and Bierlaire, M. (2016). Location choice with longitudinal WiFi data, Journal of Choice Modelling 18: 1–17. URL: https://dx.doi. ...

2015

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Matteo Dal Peraro

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Meth ...

Cell Press2015

Modeling annotator behaviors for crowd labeling

Oya Aran

Machine learning applications can benefit greatly from vast amounts of data, provided that reliable labels are available. Mobilizing crowds to annotate the unlabeled data is a common solution. Although the labels provided by the crowd are subjective and no ...

2015

WASP: Scalable Bayes via barycenters of subset posteriors

Volkan Cevher, Quoc Tran Dinh

The promise of Bayesian methods for big data sets has not fully been realized due to the lack of scalable computational algorithms. For massive data, it is necessary to store and process subsets on different machines in a distributed manner. We propose a s ...

2015

The data processing inequality and environmental model prediction

Steven Vincent Weijs

Prediction in environmental systems, such as hydrological streamflow prediction, is a challenging task. Although on a small scale, many of the physical processes are well described, accurate predictions of macroscopical (e.g. catchment scale) behavior with ...

2014

Towards Ontology-based Context Modeling for Manufacturing Applications

Drazen Nadoveza

Relevant and readily available information is a crucial basis for decision making, problem solving or performing knowledge-intensive work. The motivation for this thesis lays in the fact that in certain situations not all information provided by an applica ...

EPFL2014

A Probabilistic Approach to Mining Mobile Phone Data Sequences

Daniel Gatica-Perez, Katayoun Farrahi

We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suff ...

2014