Publications related to Reconstruction of irregularly sampled discrete-time bandlimited signals with unknown sampling locations

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

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Aude Billard, Nadia Barbara Figueroa Fernandez, Mikhail Koptev

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Sage Publications Ltd2024

Identification of typical district configurations: A two-step global sensitivity analysis framework

François Maréchal, Jonas Schnidrig, Cédric Terrier

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Pergamon-Elsevier Science Ltd2024

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Tianshu Yang, Xue Zhang

In this paper, we propose an analytical stochastic dynamic programming (SDP) algorithm to address the optimal management problem of price-maker community energy storage. As a price-maker, energy storage smooths price differences, thus decreasing energy arb ...

China Electric Power Research Inst2024

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal, Jonas Richiardi

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

Wiley2024

Reliable data-driven decision-making through optimal transport

Bahar Taskesen

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks the ...

EPFL2024

Unlabeled Principal Component Analysis and Matrix Completion

Yunzhen Yao, Liangzu Peng

We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

Microtome Publ2024

Graph generative deep learning models with an application to circuit topologies

Igor Krawczuk

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 moder ...

EPFL2024

Quantum Protocols for ML, Physics, and Finance

Grzegorz Adam Gluch

In this thesis, we give new protocols that offer a quantum advantage for problems in ML, Physics, and Finance.Quantum mechanics gives predictions that are inconsistent with local realism.The experiment proving this fact (Bell, 1964) gives a quantum protoco ...

EPFL2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Reconstruction of irregularly sampled discrete-time bandlimited signals with unknown sampling locations

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

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Identification of typical district configurations: A two-step global sensitivity analysis framework

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Reliable data-driven decision-making through optimal transport

Unlabeled Principal Component Analysis and Matrix Completion

Graph generative deep learning models with an application to circuit topologies

Quantum Protocols for ML, Physics, and Finance

Efficient Continual Finite-Sum Minimization

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

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Identification of typical district configurations: A two-step global sensitivity analysis framework

Energy Management of Price-Maker Community Energy Storage by Stochastic Dynamic Programming

Quantum Protocols for ML, Physics, and Finance

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Reliable data-driven decision-making through optimal transport

Unlabeled Principal Component Analysis and Matrix Completion

Graph generative deep learning models with an application to circuit topologies

Efficient Continual Finite-Sum Minimization