Publications related to Sampling can be faster than optimization

Reliable data-driven decision-making through optimal transport

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

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

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

COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION

Laura Grigori

Multiple tensor-times-matrix (Multi-TTM) is a key computation in algorithms for computing and operating with the Tucker tensor decomposition, which is frequently used in multidimensional data analysis. We establish communication lower bounds that determine ...

Philadelphia2024

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Philippe Schwaller, Jeff Guo

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

Amer Chemical Soc2024

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

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Silvestro Micera, Simone Romeni

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

2024

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

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, José Angel Miranda Calero, Jonathan Dan

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

2024

50 years of behavioral models for transportation and logistics

Michel Bierlaire, Léa Massé Ricard

Fifty years ago, transportation and logistics problems were primarily analyzed either from a supply-side or a demand-side perspective, with the fields of operations research and demand modeling evolving separately. Since then, there has been a growing inte ...

2024

Sampling can be faster than optimization

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Reliable data-driven decision-making through optimal transport

Graph generative deep learning models with an application to circuit topologies

Efficient Continual Finite-Sum Minimization

COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

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

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Quantum Protocols for ML, Physics, and Finance

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

50 years of behavioral models for transportation and logistics

Graph generative deep learning models with an application to circuit topologies

Reliable data-driven decision-making through optimal transport

Efficient Continual Finite-Sum Minimization

COMMUNICATION LOWER BOUNDS AND OPTIMAL ALGORITHMS FOR MULTIPLE TENSOR-TIMES-MATRIX COMPUTATION

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

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

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Quantum Protocols for ML, Physics, and Finance

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

50 years of behavioral models for transportation and logistics