Publications related to Estimation in Functional Lagged Regression

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

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 ...

EPFL2024

Distributional Regression and Autoregression via Optimal Transport

Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

EPFL2023

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Pascal Fua, Sean Lewis Hill, Jiancheng Yang, Xiang Li, Amos Sironi, Jian Zhou, Jie Zhou, Siqi Liu

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in m ...

NATURE PORTFOLIO2023

Distribution-on-distribution regression via optimal transport maps

Victor Panaretos, Laya Ghodrati

We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...

OXFORD UNIV PRESS2022

Sparsely Observed Functional Time Series: Theory and Applications

Tomas Rubin

Functional time series is a temporally ordered sequence of not necessarily independent random curves. While the statistical analysis of such data has been traditionally carried out under the assumption of completely observed functional data, it may well ha ...

EPFL2021

Neural controlled differential equations for crop classification

Accurate and scalable crop classification is important for food security and sustainable resources management. The temporal development of crops, i.e., their phenology, is a continuous phenomena that if properly captured, can help to discern them. The nove ...

2021

Physics-Inspired Structural Representations for Molecules and Materials

Michele Ceriotti, Félix Benedito Clément Musil, Andrea Grisafi

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

AMER CHEMICAL SOC2021

Adaptive Approximation of Shapes

We consider scalar-valued shape functionals on sets of shapes which are small perturbations of a reference shape. The shapes are described by parameterizations and their closeness is induced by a Hilbert space structure on the parameter domain. We justify ...

TAYLOR & FRANCIS INC2021

Structure-property maps with Kernel principal covariates regression

Michele Ceriotti, Guillaume André Jean Fraux, Benjamin Aaron Helfrecht

Data analyses based on linear methods constitute the simplest, most robust, and transparent approaches to the automatic processing of large amounts of data for building supervised or unsupervised machine learning models. Principal covariates regression (PC ...

2020

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

Juan Ramón Troncoso-Pastoriza

Background Privacy-preserving computations on genomic data, and more generally on medical data, is a critical path technology for innovative, life-saving research to positively and equally impact the global population. It enables medical research algorithm ...

2020

Estimation in Functional Lagged Regression

Graph Chatbot

Chat with Graph Search

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

Distributional Regression and Autoregression via Optimal Transport

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

Distribution-on-distribution regression via optimal transport maps

Sparsely Observed Functional Time Series: Theory and Applications

Neural controlled differential equations for crop classification

Physics-Inspired Structural Representations for Molecules and Materials

Adaptive Approximation of Shapes

Structure-property maps with Kernel principal covariates regression

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

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

Structure-property maps with Kernel principal covariates regression

Neural controlled differential equations for crop classification

Sparsely Observed Functional Time Series: Theory and Applications

Distribution-on-distribution regression via optimal transport maps

Distributional Regression and Autoregression via Optimal Transport

Physics-Inspired Structural Representations for Molecules and Materials

Adaptive Approximation of Shapes

Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets