Publications associées à Méthode hill-climbing

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

We propose ordering-based approaches for learning the maximal ancestral graph (MAG) of a structural equation model (SEM) up to its Markov equivalence class (MEC) in the presence of unobserved variables. Existing ordering-based methods in the literature rec ...

Association for the Advancement of Artificial Intelligence (AAAI)2023

Enriching the Computational Toolbox for Organocatalysis

Simone Gallarati

Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

EPFL2023

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Nirupam Gupta

In this article, we study the problem of Byzantine fault-tolerance in a federated optimization setting, where there is a group of agents communicating with a centralized coordinator. We allow up to

f

Byzantine-faulty agents, which may not follow a prescr ...

Piscataway2023

Dual-function engineering to construct ultra-stable anodes for potassium-ion hybrid capacitors: N, O-doped porous carbon spheres

Yanan Chen, Kangning Zhao, Chao Yang, Jie Xu, Xin Peng, Wen Liu, Wei Zhou

Heteroatom doping engineering is deemed to be an adoptable strategy to boost the potassium (K) storage performance of carbonaceous materials. The inevitable issue for this strategy lies in the huge volume expansion originated from the large radius of K+. I ...

ELSEVIER2022

Micro-addition of Fe in highly alloyed Cu-Ti alloys to improve both formability and strength

Roland Logé, Cyril Cayron, Baptiste Thomas Jean Rouxel

Cu-Be alloys provide excellent electrical and mechanical properties, but present serious health hazards during manufacturing. Among alternative alloys, the Cu-Ti system has the highest yield strength; however, Ti cannot be easily solutionized at concentrat ...

ELSEVIER SCI LTD2022

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

Optimization of multi-group energy structures for diffusion analyses of sodium-cooled fast reactors assisted by simulated annealing - Part I: Methodology demonstration

Konstantin Mikityuk

This study presents an approach to the selection of optimal energy group structures for multi-group nodal diffusion analyses of Sodium-cooled Fast Reactor cores. The goal is to speed up calculations, particularly in transient calculations, while maintainin ...

2021

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Sylvain Calinon, Emmanuel Pignat, Hakan Girgin, Noémie Laure Gwendoline Jaquier

Learning from demonstration (LfD) is an intuitive framework allowing non-expert users to easily (re-)program robots. However, the quality and quantity of demonstrations have a great influence on the generalization performances of LfD approaches. In this pa ...

IEEE2020

Ridesourcing Systems - Simulated Annealing Algorithm for Solving the Matching and Dispatching Problem in Dynamic Large-Scale Settings

Lynn Fayed

Ridesourcing services are gaining gradual hypes with the advancements in mobile internet and technology. The objective of these services is to mainly match and dispatch operating vehicles to waiting requests within a very short duration. The expectations t ...

2020

Efficient Greedy Coordinate Descent for Composite Problems

Martin Jaggi, Sebastian Urban Stich, Anastasiia Koloskova, Sai Praneeth Reddy Karimireddy

Coordinate descent with random coordinate selection is the current state of the art for many large scale optimization problems. However, greedy selection of the steepest coordinate on smooth problems can yield convergence rates independent of the dimension ...

MICROTOME PUBLISHING2019

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Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Enriching the Computational Toolbox for Organocatalysis

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Dual-function engineering to construct ultra-stable anodes for potassium-ion hybrid capacitors: N, O-doped porous carbon spheres

Micro-addition of Fe in highly alloyed Cu-Ti alloys to improve both formability and strength

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Optimization of multi-group energy structures for diffusion analyses of sodium-cooled fast reactors assisted by simulated annealing - Part I: Methodology demonstration

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Ridesourcing Systems - Simulated Annealing Algorithm for Solving the Matching and Dispatching Problem in Dynamic Large-Scale Settings

Efficient Greedy Coordinate Descent for Composite Problems

Enriching the Computational Toolbox for Organocatalysis

Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variables

Byzantine Fault-Tolerance in Federated Local SGD Under 2f-Redundancy

Micro-addition of Fe in highly alloyed Cu-Ti alloys to improve both formability and strength

Dual-function engineering to construct ultra-stable anodes for potassium-ion hybrid capacitors: N, O-doped porous carbon spheres

Ridesourcing Systems - Simulated Annealing Algorithm for Solving the Matching and Dispatching Problem in Dynamic Large-Scale Settings

Optimization of multi-group energy structures for diffusion analyses of sodium-cooled fast reactors assisted by simulated annealing - Part I: Methodology demonstration

Efficient Greedy Coordinate Descent for Composite Problems

Active Improvement of Control Policies with Bayesian Gaussian Mixture Model

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function