Publications related to Bias (statistics)

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

Unbiasing time-dependent Variational Monte Carlo by projected quantum evolution

Giuseppe Carleo, Clemens Giuliani, Alessandro Sinibaldi, Filippo Vicentini

We analyze the accuracy and sample complexity of variational Monte Carlo approaches to simulate the dynamics of many-body quantum systems classically. By systematically studying the relevant stochastic estimators, we are able to: (i) prove that the most us ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2023

Timing and spatial selection bias in rapid extreme event attribution

Anthony Christopher Davison, Ophélia Mireille Anna Miralles

Selection bias may arise when data have been chosen in a way that subsequent analysis does not account for. Such bias can arise in climate event attribution studies that are performed rapidly after a devastating "trigger event'', whose occurrence correspon ...

ELSEVIER2023

Perturbation theory models for LSST-era galaxy clustering: Tests with subpercent mock catalog measurements in Fourier and configuration space

Jonathan Andrew Blazek

We analyze the clustering of galaxies using the z = 1.006 snapshot of the CosmoDC2 simulation, a high-fidelity synthetic galaxy catalog designed to validate analysis methods for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We prese ...

American Physical Society2022

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition

Tao Lin

In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bias caused by personal, environmental, or ...

ASSOC COMPUTING MACHINERY2022

An automatized method to determine latencies of motor-evoked potentials under physiological and pathophysiological conditions

Friedhelm Christoph Hummel, Takuya Morishita, Pierre Theopistos Vassiliadis, Andéol Geoffroy Cadic-Melchior, Claudia Bigoni

Background. Latencies of motor evoked potentials (MEPs) can provide insights into the motor neuronal pathways activated by transcranial magnetic stimulation. Notwithstanding its clinical relevance, accurate, unbiased methods to automatize latency detection ...

IOP Publishing Ltd2022

Controlling the rotation of drift tearing modes by biased electrode in ADITYA-U tokamak

Harshita Raj, Ankit Kumar, Abhijit Sen, Rohit Kumar, Sameer Kumar

The influence of background plasma poloidal rotation on the rotation frequency of the m/n = 2/1 drift tearing mode (DTM) has been studied in ADITYA-U tokamak. The poloidal rotation velocity of the background plasma in the ion diamagnetic direction is incre ...

2021

Monte Carlo Estimators for Differential Light Transport

Wenzel Alban Jakob, Tizian Lucien Zeltner, Sébastien Nicolas Speierer

Physically based differentiable rendering algorithms propagate derivatives through realistic light transport simulations and have applications in diverse areas including inverse reconstruction and machine learning. Recent progress has led to unbiased metho ...

ASSOC COMPUTING MACHINERY2021

Countering Bias in Personalized Rankings From Data Engineering to Algorithm Development

Mirko Marras

This tutorial presents recent advances on the assessment and mitigation of data and algorithmic bias in personalized rankings. We first introduce fundamental concepts and definitions associated with bias issues, covering the state of the art and describing ...

IEEE COMPUTER SOC2021

Euclid: The reduced shear approximation and magnification bias for Stage IV cosmic shear experiments

Georges Meylan, Jean-Luc Starck, Yi Wang, Richard Massey

Context. Stage IV weak lensing experiments will offer more than an order of magnitude leap in precision. We must therefore ensure that our analyses remain accurate in this new era. Accordingly, previously ignored systematic effects must be addressed.Aims. ...

EDP SCIENCES S A2020

Enhancing discrete choice models with representation learning

Alexandre Massoud Alahi, Virginie Janine Camille Lurkin

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specif ...

2020

Improving Inter-Laboratory Reproducibility in Measurement of Biochemical Methane Potential (BMP)

Christof Holliger

Biochemical methane potential (BMP) tests used to determine the ultimate methane yield of organic substrates are not sufficiently standardized to ensure reproducibility among laboratories. In this contribution, a standardized BMP protocol was tested in a l ...

2020

Controlling the bias error of Fokker- Planck methods for rarefied gas dynamics simulations

Mohammadhossein Gorji

Direct simulation Monte-Carlo (DSMC) is the most established method for rarefied gas flow simulations. It is valid from continuum to near vacuum, but in cases involving small Knudsen numbers (Kn), it suffers from high computational cost. The Fokker-Planck ...

2019

Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model

Andrea Rinaldo, Bettina Schaefli, Ana Clara Pereira Barbosa Santos

Streamflow recession analysis characterizes the storage-outflow relationship in catchments. This relationship, which typically follows a power law, summarizes all catchment-scale subsurface hydrological processes and has long been known to be a key descrip ...

2019

Brain Perfusion Measurements Using Multidelay Arterial Spin-Labeling Are Systematically Biased by the Number of Delays

Dimitri Nestor Alice Van De Ville, Nicolas Gninenko, Merel Margaretha van der Thiel

BACKGROUND AND PURPOSE:Multidelay arterial spin-labeling is a promising emerging method in clinical practice. The effect of imaging parameters in multidelay arterial spin-labeling on estimated cerebral blood flow measurements remains unknown. We directly c ...

AMER SOC NEURORADIOLOGY2018

Optimizing the acceleration and resolution of three-dimensional fat image navigators for high-resolution motion correction at 7T

José Pedro Rebelo Ferreira Marques, Daniel Gallichan

PURPOSE: To investigate the effect of spatial resolution and parallel imaging acceleration factor on the quality of the motion estimates derived from image navigators with a three-dimensional (3D) gradient-recalled echo (GRE) acquisition with fat excitatio ...

Wiley-Blackwell2017

Uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider

Mikael Johan Kuusela

This thesis studies statistical inference in the high energy physics unfolding problem, which is an ill-posed inverse problem arising in data analysis at the Large Hadron Collider (LHC) at CERN. Any measurement made at the LHC is smeared by the finite reso ...

EPFL2016

Probing the Limits of Social Data : Biases, Methods, and Domain Knowledge

Alexandra Olteanu

Online social data has been hailed to provide unprecedented insights into human phenomena due to its ability to capture human behavior at a scale and level of detail, both in breadth and depth, that is hard to achieve through conventional data collection t ...

EPFL2016

Inference for binomial probability based on dependent Bernoulli random variables with applications to meta-analysis and group level studies

Stephan Morgenthaler

We study bias arising as a result of nonlinear transformations of random variables in random or mixed effects models and its effect on inference in group-level studies or in meta-analysis. The findings are illustrated on the example of overdispersed binomi ...

Wiley-Blackwell2016

Quantifying the Bullwhip Effect Using Two-Echelon Data: A Cross-Industry Empirical Investigation

Ralf Seifert, Olov Isaksson

The bullwhip effect denotes the phenomenon whereby demand variability is amplified from a downstream site (buyer) to an upstream site (supplier) in the supply chain. This paper contributes to the literature that empirically investigates the bullwhip effect ...

Elsevier2016