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Publications associées (40)

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A Reinforcement Learning Approach to Train Routing and Scheduling

Zora Oswald

Good train scheduling for a big network with many trains is very hard to achieve. As the trains are competing for the tracks with one another, the number of constraints grows rapidly. Trying to take advantage of emerging technologies in the areas of optimi ...

2020

Separable Effects for Causal Inference in the Presence of Competing Events

Mats Julius Stensrud

In time-to-event settings, the presence of competing events complicates the definition of causal effects. Here we propose the new separable effects to study the causal effect of a treatment on an event of interest. The separable direct effect is the treatm ...

2020

A Graphical Description of Partial Exchangeability

Mats Julius Stensrud, Aaron Leor Sarvet

Partial exchangeability is sufficient for the identification of some causal effects of interest. Here we review the use of common graphical tools and the sufficient component cause model in the context of partial exchangeability. We illustrate the utility ...

2020

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees

Negar Kiyavash, Saber Salehkaleybar, Kun Zhang

A directed acyclic graph (DAG) is the most common graphical model for representing causal relationships among a set of variables. When restricted to using only observational data, the structure of the ground truth DAG is identifiable only up to Markov equi ...

AAAI Press2019

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

Ilija Bogunovic

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

EPFL2019

New Graph Algorithms via Polyhedral Techniques

Jakub Tarnawski

In this thesis we give new algorithms for two fundamental graph problems. We develop novel ways of using linear programming formulations, even exponential-sized ones, to extract structure from problem instances and to guide algorithms in making progress. S ...

EPFL2019

Efficiently Learning Fourier Sparse Set Functions

Andreas Krause, Mikhail Kapralov, Amir Zandieh

Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2019

Motility as a tool to uncover mobility practices

Vincent Kaufmann, Guillaume Simon Joseph Drevon, Alexis Gumy, Eloi Antoine Maël Bernier

This paper investigate the causal relationships that exist between the motility, defined as the personal ability to be mobile (Kaufmann et al., 2015), and mobility behaviors. To do so, we use the first wave (2018) of the "national daily mobility panel" sur ...

2019

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

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018