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A Probabilistic Approach to Socio-Geographic Reality Mining

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Data Summarization with Social Contexts

Karl Aberer, Tian Guo, Rameez Rahman, Hao Zhuang, Xia Hu

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summariza ...

Assoc Computing Machinery2016

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

Scalable Metric Learning via Weighted Approximate Rank Component Analysis

François Fleuret, Cijo Jose

We are interested in the large-scale learning of Mahalanobis distances, with a particular focus on person re-identification. We propose a metric learning formulation called Weighted Approximate Rank Component Analysis (WARCA). WARCA optimizes the precision ...

Springer Int Publishing Ag2016

Experimental study of large scale plunging jets

Michael Pfister

The lack of knowledge regarding air entrainment caused by large jets in hydraulic structures, such as downstream Pelton turbines, has led EDF (Electricité de France) to carry out a test series to optimize the La Coche Power plant. The data extracted from t ...

2016

Runtime Prediction for Scale-Out Data Analytics

Adrian Daniel Popescu

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...

EPFL2015

Correcting for endogeneity using the EMIS method: a case study with revealed preference data

Michel Bierlaire, Anna Fernández Antolín

Endogeneity is an important issue that often arises in discrete choice models leading to biased estimates of the parameters. We propose the extended multiple indicator solution (EMIS) methodology to correct for it and exemplify it with a case study using r ...

2015

Mining, Modeling and Predicting Mobility

Mohamed Kafsi

Mobility is a central aspect of our life, and our movements reveal much more about us than simply our whereabouts. In this thesis, we are interested in mobility and study it from three different perspectives: the modeling perspective, the information-theor ...

EPFL2015

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

Scalable Collaborative Bayesian Preference Learning

Matthias Seeger, Mohammad Emtiyaz Khan, Young Jun Ko

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014