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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

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