Publications associées à Processus empirique

Scalable constrained optimization

Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...

EPFL2024

Asset purchases, limited asset markets participation and inequality

Stylianos Tsiaras

This paper analyses the effects of quantitative easing (QE) on households' income and consumption inequality in the Euro Area. Using a SVAR with high frequency identification, I show that an identified QE shock is redistributive and expansionary. To ration ...

ELSEVIER2023

Nothing New Under the Sun: Novelty Constructs and Measures in Social Studies

Davide Bavato

The concept of novelty is central to questions of creativity, innovation, and discovery. Despite the prominence in scientific inquiry and everyday discourse, there is a chronic ambiguity over its meaning and a surprising variety of empirical measures, whic ...

Emerald Publishing Limited2022

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

Juan Pablo Madrigal Cianci

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP). The first hierarchical method we present is base ...

EPFL2022

Slim: Explicit Slot-Intent Mapping With Bert For Joint Multi-Intent Detection And Slot Filling

Boi Faltings, Fei Mi, Fengyu Cai, Wanhao Zhou

Utterance-level intent detection and token-level slot filling are two key tasks for spoken language understanding (SLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often mul ...

IEEE2022

Scaling Description of Creep Flow in Amorphous Solids

Matthieu Wyart, Thomas Willem Jan de Geus, Wencheng Ji, Marko Popovic

Amorphous solids such as coffee foam, toothpaste, or mayonnaise display a transient creep flow when a stress E is suddenly imposed. The associated strain rate is commonly found to decay in time as gamma_ -t-nu, followed either by arrest or by a sudden flui ...

AMER PHYSICAL SOC2022

An Empirical and Subjective Model of Upper Extremity Fatigue Under Hypogravity

Volker Gass, Claude Nicollier, Tatiana Maillard

In the context of extra-terrestrial missions, the effects of hypogravity (0 < G < 1) on the human body can reduce the well-being of the crew, cause musculoskeletal problems and affect their ability to perform tasks, especially during long-term missions. To ...

FRONTIERS MEDIA SA2022

Theory of Deep Learning: Neural Tangent Kernel and Beyond

Arthur Ulysse Jacot-Guillarmod

In the recent years, Deep Neural Networks (DNNs) have managed to succeed at tasks that previously appeared impossible, such as human-level object recognition, text synthesis, translation, playing games and many more. In spite of these major achievements, o ...

EPFL2022

Dynamic Effective Porosity Explains Laboratory Experiments on Watertable Fluctuations in Coastal Unconfined Aquifers

David Andrew Barry, Zhaoyang Luo

Watertable fluctuations are a characteristic feature of coastal unconfined aquifers. They interact with the vadose zone creating a dynamic effective porosity, for which a new (empirical) expression is proposed based on a dimensionless parameter related to ...

2023

Multiple testing with test statistics following heavy-tailed distributions

Zhiwen Jiang

In multiple testing problems where the components come from a mixture model of noise and true effect, we seek to first test for the existence of the non-zero components, and then identify the true alternatives under a fixed significance level

\alpha

. Two ...

EPFL2021

Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic ...

Microtome Publishing2021

Landmark Regularization: Ranking Guided Super-Net Training in Neural Architecture Search

Mathieu Salzmann, Kaicheng Yu

Weight sharing has become a de facto standard in neural architecture search because it enables the search to be done on commodity hardware. However, recent works have empirically shown a ranking disorder between the performance of stand-alone architectures ...

IEEE COMPUTER SOC2021

From Data to Decisions: Distributionally Robust Optimization is Optimal

Daniel Kuhn, Peyman Mohajerin Esfahani

We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...

2021

Wasserstein Distributionally Robust Learning

Soroosh Shafieezadeh Abadeh

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

EPFL2020

Expectations with Endogenous Information Acquisition: An Experimental Investigation

Andreas Fuster

We use a survey experiment to generate direct evidence on how people acquire and process information. Participants can buy different information signals that could help them forecast future national home prices. We elicit their valuations and exogenously v ...

2022

Regularization via Mass Transportation

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, ...

2019

A central limit theorem for functions of stationary max-stable random fields on R-d

Erwan Fabrice Koch, Clément Dombry

Max-stable random fields are very appropriate for the statistical modelling of spatial extremes. Hence, integrals of functions of max-stable random fields over a given region can play a key role in the assessment of the risk of natural disasters, meaning t ...

ELSEVIER2019

Numerical Simulations of Pulsating Heat Pipes, Part 1: Modeling

John Richard Thome, Jackson Braz Marcinichen, Philippe Aubin, Filippo Cataldo, Brian Paul D'Entremont, Raffaele Luca Amalfi

The pulsating nature of a closed loop pulsating heat pipe (CLPHP) makes the prediction of its thermal-hydraulic performance extremely challenging. Unfortunately, the design of CLPHPs is still quite an empirical process, requiring testing of many prototypes ...

IEEE2019

The unfolding argument: Why IIT and other causal structure theories cannot explain consciousness

Michael Herzog, Kathryn Hess Bellwald, Adrien Christophe Doerig, Aaron Schurger

How can we explain consciousness? This question has become a vibrant topic of neuroscience research in recent decades. A large body of empirical results has been accumulated, and many theories have been proposed. Certain theories suggest that consciousness ...

2019

The mean-field behavior of processor sharing systems with general job lengths under the SQ(d) policy

Arpan Mukhopadhyay

This paper addresses the mean-field behavior of large-scale systems of parallel servers with a processor sharing service discipline when arrivals are Poisson and jobs have general service time distributions when an SQ(d) routing policy is used. Under this ...

ELSEVIER SCIENCE BV2018