Publications related to Data-based model maintenance in the era of industry 4.0: A methodology

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Pascal Frossard, Roberto Gerson De Albuquerque Azevedo, Chaofan He

Omnidirectional video streaming is usually implemented based on the representations of tiles, where the tiles are obtained by splitting the video frame into several rectangular areas and each tile is converted into multiple representations with different r ...

Piscataway2023

Stochastic Models for Comparison-based Search

Daniyar Chumbalov

In this thesis we study a problem of searching in a space of objects using comparisons. To navigate through the space to the target object

t

, we ask a sequence of questions of the form ``which object

i

or

j

is closer to

t

?'' for which we observe no ...

EPFL2023

Memory of Motion for Initializing Optimization in Robotics

Teguh Santoso Lembono

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

EPFL2022

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Paul Thierry Yves Rolland

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

EPFL2022

Utility/privacy trade-off as regularized optimal transport

Etienne Patrice Boursier

Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information by taking full advant ...

SPRINGER HEIDELBERG2022

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Colin Neil Jones, Yuning Jiang, Paul Scharnhorst, Emilio Maddalena

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...

2021

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Mario Paolone, Mokhtar Bozorg, Mostafa Nick

In this paper we present an adaptive robust optimization framework for the day-ahead scheduling of Active Distribution Networks (ADNs) where the controlled devices are distributed Energy Storage Systems (ESSs). First, the targeted problem is formulated usi ...

IEEE2019

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Dominique Bonvin, Julien Léo Billeter, Diogo Filipe Mateus Rodrigues

For lumped homogeneous reaction systems, this paper presents a kinetic model identification scheme that provides maximum-likelihood parameter estimates and guarantees convergence to global optimality. The use of the extent-based incremental approach allows ...

PERGAMON-ELSEVIER SCIENCE LTD2019

Exploiting structure of chance constrained programs via submodularity

Maryam Kamgarpour, Tony Alan Wood

We introduce a novel approach to reduce the computational effort of solving convex chance constrained programs through the scenario approach. Instead of reducing the number of required scenarios, we directly minimize the computational cost of the scenario ...

2019

Scalable Convex Optimization Methods for Semidefinite Programming

Alp Yurtsever

With the ever-growing data sizes along with the increasing complexity of the modern problem formulations, contemporary applications in science and engineering impose heavy computational and storage burdens on the optimization algorithms. As a result, there ...

EPFL2019

Data-based model maintenance in the era of industry 4.0: A methodology

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Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Stochastic Models for Comparison-based Search

Memory of Motion for Initializing Optimization in Robotics

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Utility/privacy trade-off as regularized optimal transport

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Exploiting structure of chance constrained programs via submodularity

Scalable Convex Optimization Methods for Semidefinite Programming

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Memory of Motion for Initializing Optimization in Robotics

Maximum-likelihood estimation of kinetic parameters via the extent-based incremental approach

Scalable Convex Optimization Methods for Semidefinite Programming

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

Exploiting structure of chance constrained programs via submodularity

Stochastic Models for Comparison-based Search

Utility/privacy trade-off as regularized optimal transport

A Robust Optimization Framework for the Day-Ahead Scheduling of Active Distribution Networks including Energy Storage Systems