Linking response quality to survey engagement: A combined random scale and latent variable approach

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

Point identification of causal effects requires strong assumptions that are unreasonable in many practical settings. However, bounds on these effects can often be derived under plausible assumptions. Even when these bounds are wide or cover null effects, t ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

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 ...

This paper studies the infinite-width limit of deep linear neural networks (NNs) initialized with random parameters. We obtain that, when the number of parameters diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a ...

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

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 ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

Modern power distribution systems are experiencing a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). Their presence complicates the analysis and mitigation of harmonics, whose creation and propagation may be amplified ...

Photoplethysmography (PPG) is a widely emerging method to assess vascular health in humans. The origins of the signal of reflective PPG on peripheral arteries have not been thoroughly investigated. We aimed to identify and quantify the optical and biomecha ...

High-level waste, stemming from nuclear electricity generation poses significant environmental and safety concerns. Currently, high-level wastes are stored in interim facilities needing constant monitoring and waiting for a definitive solution. Deep geolog ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

To obtain an accurate cosmological inference from upcoming weak lensing surveys such as the one conducted by Euclid, the shear measurement requires calibration using galaxy image simulations. As it typically requires millions of simulated galaxy images and ...

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 ...

In the field of choice modeling, the availability of ever-larger datasets has the potential to significantly expand our understanding of human behavior, but this prospect is limited by the poor scalability of discrete choice models (DCMs): as sample sizes ...

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

This article proposes methods to model non-stationary temporal graph processes motivated by a hospital interaction data set. This corresponds to modelling the observation of edge variables indicating interactions between pairs of nodes exhibiting dependenc ...

In this paper, we present a spatial branch and bound algorithm to tackle the continuous pricing problem, where demand is captured by an advanced discrete choice model (DCM). Advanced DCMs, like mixed logit or latent class models, are capable of modeling de ...

Linking response quality to survey engagement: A combined random scale and latent variable approach

Chat with Graph Search

Probing the latent hierarchical structure of data via diffusion models

Improved bounds and inference on optimal regimes

Graph generative deep learning models with an application to circuit topologies

Robust machine learning for neuroscientific inference

Extensions of Peer Prediction Incentive Mechanisms

Infinite-width limit of deep linear neural networks

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Spectral Estimators for High-Dimensional Matrix Inference

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Unified Harmonic Power-Flow and Stability Analysis of Power Grids with Converter-Interfaced Distributed Energy Resources

Quantification of the Phenomena Affecting Reflective Arterial Photoplethysmography

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Euclid: Improving the efficiency of weak lensing shear bias calibration

Timing and spatial selection bias in rapid extreme event attribution

Stochastic adaptive resampling for the estimation of discrete choice models

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Networks with correlated edge processes

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling