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Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

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Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

Comparing methodologies for structural identification and fatigue life prediction of a highway bridge

Ian Smith, Alain Nussbaumer, Sai Ganesh Sarvotham Pai

Accurate measurement-data interpretation leads to increased understanding of structural behavior and enhanced asset-management decision making. In this paper, four data-interpretation methodologies, residual minimization, traditional Bayesian model updatin ...

2018

Validation of probabilistic classifiers

Michel Bierlaire, Timothy Michael Hillel

Non-parametric probabilistic classification models are increasingly being investigated as an alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...

2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...

ASSOC COMPUTING MACHINERY2018

Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

ACM2018

Approximate maximum likelihood estimation for population genetic inference

Gregory Bruce Ewing

In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. Howev ...

De Gruyter2017

Efficient inference for genetic association studies with multiple outcomes

Anthony Christopher Davison, Jörg Hager, Hélène Ruffieux

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on ma ...

Oxford University Press2017

A Bayesian view of doubly robust causal inference

In causal inference the effect of confounding may be controlled using regression adjustment in an outcome model, propensity score adjustment, inverse probability of treatment weighting or a combination of these. Approaches based on modelling the treatment ...

Oxford University Press2016

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We consider the sequential Bayesian optimization problem with bandit feedback, adopting a formulation that allows for the reward function to vary with time. We model the reward function using a Gaussian process whose evolution obeys a simple Markov model. ...

2016

Fast and Accurate Inference of Plackett-Luce Models

Matthias Grossglauser, Lucas Maystre

We show that the maximum-likelihood (ML) estimate of models derived from Luce’s choice axiom (e.g., the Plackett–Luce model) can be expressed as the stationary distribution of a Markov chain. This conveys insight into several recently proposed spectral inf ...

2015