Publications related to Statistical methods for insect choice experiments

Spectral Estimators for High-Dimensional Matrix Inference

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

EPFL2024

Extensions of Peer Prediction Incentive Mechanisms

Adam Julian Richardson

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Mario Paolone, Vladimir Sovljanski

Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) are widely used to characterize the impedance and estimate parameters of electrochemical systems such as batteries. We use a generic ECM with ten parameters grouped to model ...

2024

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Mats Julius Stensrud, Matias Janvin

We summarize what we consider to be the two main limitations of the "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event" (Schmidli et al. 2022). First, the authors did not give detailed guidance on how to choose an appropriate esti ...

TAYLOR & FRANCIS INC2023

Social Workers and Irregular Migrants in the Assistance Circuit: Making Sense of Paradoxical Inclusion

Maxime Carl Felder, Joan Stavo-Debauge, Sahar Fneich

Despite restrictive policy frameworks, cities sometimes provide support to irregular migrants. Scholars have analysed these forms of inclusion, focusing on policies and tensions between inclusionary approaches by local or urban actors and exclusionary appr ...

2023

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Andrea Zanoni

We study the problem of learning unknown parameters of stochastic dynamical models from data. Often, these models are high dimensional and contain several scales and complex structures. One is then interested in obtaining a reduced, coarse-grained descript ...

EPFL2022

Adversarial Parametric Pose Prior

Pascal Fua, Mathieu Salzmann, Anastasia Remizova, Andrey Davydov, Victor Constantin, Sina Honari

The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is und ...

IEEE2022

Statistical limits of high-dimensional inference problems

Clément Dominique Luneau

This thesis focuses on two kinds of statistical inference problems in signal processing and data science. The first problem is the estimation of a structured informative tensor from the observation of a noisy tensor in which it is buried. The structure com ...

EPFL2021

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

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Statistical methods for insect choice experiments

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Spectral Estimators for High-Dimensional Matrix Inference

Extensions of Peer Prediction Incentive Mechanisms

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Social Workers and Irregular Migrants in the Assistance Circuit: Making Sense of Paradoxical Inclusion

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Adversarial Parametric Pose Prior

Statistical limits of high-dimensional inference problems

Multiple testing with test statistics following heavy-tailed distributions

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Spectral Estimators for High-Dimensional Matrix Inference

On the use of Cramér-Rao Lower Bound for least-variance circuit parameters identification of Li-ion cells

Extensions of Peer Prediction Incentive Mechanisms

We Need Subject Matter Expertise to Choose and Identify Causal Estimands: Comment on "Estimands for Recurrent Event Endpoints in the Presence of a Terminal Event"

Adversarial Parametric Pose Prior

Statistical limits of high-dimensional inference problems

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes

Multiple testing with test statistics following heavy-tailed distributions

Social Workers and Irregular Migrants in the Assistance Circuit: Making Sense of Paradoxical Inclusion