Publications related to Process Design and Optimization (MLS-S03): a Journey in Modeling, Optimization and Regression

Distributional Regression and Autoregression via Optimal Transport

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

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

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

Minimax rate for optimal transport regression between distributions

Victor Panaretos, Laya Ghodrati

Distribution-on-distribution regression considers the problem of formulating and es-timating a regression relationship where both covariate and response are probability distributions. The optimal transport distributional regression model postulates that th ...

ELSEVIER2022

One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction

Mathias Josef Payer

Fuzzing is the de-facto default technique to discover software flaws, randomly testing programs to discover crashing test cases. Yet, a particular scenario may only care about specific code regions (for, e.g., bug reproduction, patch or regression testing) ...

ASSOC COMPUTING MACHINERY2022

Gaussian Process Regression for Materials and Molecules

Michele Ceriotti, David Mark Wilkins

We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...

AMER CHEMICAL SOC2021

Functional lagged regression with sparse noisy observations

Victor Panaretos, Tomas Rubin

A functional (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions. In practice, the underlying regressor curve time series are not always ...

WILEY2020

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Volkan Cevher, Junhong Lin

We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...

2020

A memory of motion for visual predictive control tasks

Sylvain Calinon, Antonio Paolillo, Teguh Santoso Lembono

This paper addresses the problem of efficiently achieving visual predictive control tasks. To this end, a memory of motion, containing a set of trajectories built off-line, is used for leveraging precomputation and dealing with difficult visual tasks. Stan ...

2020

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Anthony Christopher Davison, Jörg Hager, Hélène Ruffieux

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...

INST MATHEMATICAL STATISTICS2020

Process Design and Optimization (MLS-S03): a Journey in Modeling, Optimization and Regression

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Distributional Regression and Autoregression via Optimal Transport

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Distribution-on-distribution regression via optimal transport maps

Minimax rate for optimal transport regression between distributions

One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction

Gaussian Process Regression for Materials and Molecules

Functional lagged regression with sparse noisy observations

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

A memory of motion for visual predictive control tasks

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

One Fuzz Doesn’t Fit All: Optimizing Directed Fuzzing via Target-tailored Program State Restriction

Minimax rate for optimal transport regression between distributions

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Distribution-on-distribution regression via optimal transport maps

Distributional Regression and Autoregression via Optimal Transport

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Gaussian Process Regression for Materials and Molecules

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

Functional lagged regression with sparse noisy observations

A memory of motion for visual predictive control tasks