Publications associées à Fat-tailed distribution

Exploiting the Signal-Leak Bias in Diffusion Models

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert, Athanasios Fitsios

There is a bias in the inference pipeline of most diffusion models. This bias arises from a signal leak whose distribution deviates from the noise distribution, creating a discrepancy between training and inference processes. We demonstrate that this signa ...

2024

Machine Learning for Modeling Stock Returns

Teng Andrea Xu

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

EPFL2024

Current propagation type self-consistent leader-return stroke model

Farhad Rachidi-Haeri, Marcos Rubinstein

A current propagation type return stroke model which is consistent with the estimated distribution of the charge on the leader channel is described. The model takes into account the dispersion of the return stroke current along the return stroke channel. T ...

Elsevier2023

Hierarchical processing underpins competition in tactile perceptual bistability

Andrea Ferrario

Ambiguous sensory information can lead to spontaneous alternations between perceptual states, recently shown to extend to tactile perception. The authors recently proposed a simplified form of tactile rivalry which evokes two competing percepts for a fixed ...

SPRINGER2023

Learning From Heterogeneous Data Based on Social Interactions Over Graphs

Ali H. Sayed, Virginia Bordignon, Stefan Vlaski

This work proposes a decentralized architecture, where individual agents aim at solving a classification problem while observing streaming features of different dimensions and arising from possibly different distributions. In the context of social learning ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

A Statistical Framework to Investigate the Optimality of Signal-Reconstruction Methods

Michaël Unser, Pakshal Narendra Bohra, Pol del Aguila Pla

We present a statistical framework to benchmark the performance of reconstruction algorithms for linear inverse problems, in particular, neural-network-based methods that require large quantities of training data. We generate synthetic signals as realizati ...

2023

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

Daniel Kuhn, Zhi Chen, Wolfram Wiesemann

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

2023

Functional peaks-over-threshold analysis

Anthony Christopher Davison, Raphaël Gérard Théodore Michel Marie de Deloÿe et Fourcade de Fondeville

Peaks-over-threshold analysis using the generalised Pareto distribution is widely applied in modelling tails of univariate random variables, but much information may be lost when complex extreme events are studied using univariate results. In this paper, w ...

WILEY2022

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Florent Gérard Krzakala, Alia Abbara

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we g ...

2022

On the Use of Benford’s Law to Assess the Quality of the Data Provided by Lightning Locating Systems

Farhad Rachidi-Haeri, Marcos Rubinstein, Amirhossein Mostajabi, Wolfgang Schulz

Lightning causes significant damage and casualties globally by directly striking humans and livestock, by igniting forest fires, and by inducing electrical surges in electronic infrastructure, airplanes, rockets, etc. Monitoring the evolution of thundersto ...

2022

A Long Time Ago in a Galaxy Far, Far Away: A Candidate z similar to 12 Galaxy in Early JWST CEERS Imaging

Michaela Hirschmann

We report the discovery of a candidate galaxy with a photo -z of z similar to 12 in the first epoch of the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science Survey. Following conservative selection criteria, we identify a source with ...

IOP Publishing Ltd2022

Causal modelling of heavy-tailed variables and confounders with application to river flow

Anthony Christopher Davison, Valérie Chavez

Confounding variables are a recurrent challenge for causal discovery and inference. In many situations, complex causal mechanisms only manifest themselves in extreme events, or take simpler forms in the extremes. Stimulated by data on extreme river flows a ...

SPRINGER2022

A Current Propagation Type Return Stroke Model Consistent with the Leader Charge Distribution

Farhad Rachidi-Haeri, Marcos Rubinstein

We describe a current propagation type return stroke model which is consistent with the estimated distribution of the charge on the leader channel. The model takes into account the dispersion of the return stroke current along the return stroke channel. Th ...

2021

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Mathieu Salzmann, Fatemehsadat Saleh

Despite the recent advances in multiple object tracking (MOT), achieved by joint detection and tracking, dealing with long occlusions remains a challenge. This is due to the fact that such techniques tend to ignore the long-term motion information. In this ...

IEEE COMPUTER SOC2021

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

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Bahar Taskesen

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, ...

2023

Spatial evaluation of potential saturation and contrast effects of discomfort glare in an open-plan office

Marilyne Andersen, Jan Wienold, Geraldine Cai Ting Quek, Stephen William Wasilewski

This paper analyzes the relative distribution of annual saturation and contrast effects of discomfort glare in a deep open-plan office. A previous comparative study has shown that while hybrid glare metrics were found to predict glare well in most scenario ...

2021

Effect of collisions on non-adiabatic electron dynamics in ITG-driven microturbulence

Stephan Brunner, Justin Richard Ball

Non-adiabatic electron response leads to significant changes in ion temperature gradient (ITG) eigenmodes, leading, in particular, to fine-structures that are significantly extended along the magnetic field lines at corresponding mode rational surfaces (MR ...

2021

True scale-free networks hidden by finite size effects

Andrea Rinaldo, Samir Suweis, Amos Maritan

We analyze about 200 naturally occurring networks with distinct dynamical origins to formally test whether the commonly assumed hypothesis of an underlying scale-free structure is generally viable. This has recently been questioned on the basis of statisti ...

National Academy of Sciences2021

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2022