Publications related to Stochastic adaptive resampling for the estimation of discrete choice models

Dynamic Monte Carlo reactor modeling of calcium looping with sorbent purge and utilization decay

This work presents a new dynamic modelling approach for calcium looping systems that allows explicit sorbent deactivation and purge/makeup. These are common in plant operations, but often neglected in modelling. This model adopts a Monte Carlo approach, tr ...

2022

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Fabio Nobile, Sebastian Krumscheid, Matthieu Claude Martin

We consider the numerical approximation of an optimal control problem for an elliptic Partial Differential Equation (PDE) with random coefficients. Specifically, the control function is a deterministic, distributed forcing term that minimizes the expected ...

2021

Self-learning surrogate models in superstructure optimization

François Maréchal, Ivan Daniel Kantor, Julia Granacher, Michel Lopez

In this contribution, we propose an algorithm for replacing non-linear process simulation integrated in multi-level optimization of an energy system superstructure with surrogate models. With our approach, we demonstrate that surrogate models are a valid t ...

Elsevier2021

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Giuseppe Carleo, Julien Sebastian Gacon, Stefan Woerner

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computat ...

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF2021

Passenger-centric timetable rescheduling: A user equilibrium approach

Michel Bierlaire, Shadi Sharif Azadeh, Mohammad Yousef Maknoon, Stefan Yves Gabriel Binder

Unexpected disruptions commonly occur in the railway network, causing delays, and extra cost for operators and inconvenience for passengers by missing their connection and facing overcrowded trains. This paper presents a passenger-centric approach for time ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Zhen Wei, Zhiye Wang, Peixia Li

Person retrieval aims at effectively matching the pedestrian images over an extensive database given a specified identity. As extracting effective features is crucial in a high-performance retrieval system, recent significant progress was achieved by part- ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Timothy Michael Hillel, Virginie Janine Camille Lurkin, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2020

Multi-armed Bandits in Action

Farnood Salehi

Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...

EPFL2020

Forward-Backward Splitting for Optimal Transport Based Problems

Pascal Frossard, Guillermo Ortiz Jimenez, Mireille El Gheche, Hermina Petric Maretic, Effrosyni Simou

Optimal transport aims to estimate a transportation plan that minimizes a displacement cost. This is realized by optimizing the scalar product between the sought plan and the given cost, over the space of doubly stochastic matrices. When the entropy regula ...

2020

Stochastic adaptive resampling for the estimation of discrete choice models

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Dynamic Monte Carlo reactor modeling of calcium looping with sorbent purge and utilization decay

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Self-learning surrogate models in superstructure optimization

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Passenger-centric timetable rescheduling: A user equilibrium approach

Convergence of adaptive algorithms for constrained weakly convex optimization

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Multi-armed Bandits in Action

Forward-Backward Splitting for Optimal Transport Based Problems

Dynamic Monte Carlo reactor modeling of calcium looping with sorbent purge and utilization decay

Complexity analysis of stochastic gradient methods for PDE-constrained optimal control problems with uncertain parameters

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Forward-Backward Splitting for Optimal Transport Based Problems

Multi-armed Bandits in Action

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

Passenger-centric timetable rescheduling: A user equilibrium approach

Self-learning surrogate models in superstructure optimization

Convergence of adaptive algorithms for constrained weakly convex optimization

Adaptive and Robust Partition Learning for Person Retrieval With Policy Gradient