AbstractVisual detection and discrimination thresholds are often measured using adaptive staircases, and most studies use transformed (or weighted) up/down methods with fixed step sizes—in the spirit of Wetherill and Levitt (Br J Mathemat Statist Psychol 1965;18:1–10) or Kaernbach (Percept Psychophys 1991;49:227–229)—instead of changing step size at each trial in accordance with best-placement rules—in the spirit of Watson and Pelli (Percept Psychophys 1983;47:87–91). It is generally assumed that a fixed-step-size (FSS) staircase converges on the stimulus level at which a correct response occurs with the probabilities derived by Wetherill and Levitt or Kaernbach, but this has never been proved rigorously. This work used simulation technique...
The study of evolutionary dynamics increasingly relies on computational methods, as more and more ca...
An influential paper by Kleibergen (2005) introduces Lagrange multiplier (LM) and conditional likeli...
International audienceA longstanding problem in sequential Monte Carlo (SMC) is to mathematically pr...
AbstractVisual detection and discrimination thresholds are often measured using adaptive staircases,...
Despite the widespread use of up-down staircases in adaptive threshold estimation, their efficiency ...
When measuring thresholds, careful selection of stimulus amplitude can increase efficiency by increa...
AbstractSince the work by Miller, Amon, and Reinhardt, which correctly warned against the indiscrimi...
International audienceThis paper presents a refined single parent evolution strategy that is derando...
For algorithms of the Robbins-Monro type, the best choice (from the asymptotic point of view) for th...
Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models....
International audienceStep-size adaptation for randomised search algorithms like evolution strategie...
In recent years several proposals for the step-size selection have largely improved the gradient me...
Bayesian staircases are widely used in psychophysics to estimate detection thresholds. Simulations h...
International audienceWe consider the least-squares regression problem and provide a detailed asympt...
International audienceThe CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, wher...
The study of evolutionary dynamics increasingly relies on computational methods, as more and more ca...
An influential paper by Kleibergen (2005) introduces Lagrange multiplier (LM) and conditional likeli...
International audienceA longstanding problem in sequential Monte Carlo (SMC) is to mathematically pr...
AbstractVisual detection and discrimination thresholds are often measured using adaptive staircases,...
Despite the widespread use of up-down staircases in adaptive threshold estimation, their efficiency ...
When measuring thresholds, careful selection of stimulus amplitude can increase efficiency by increa...
AbstractSince the work by Miller, Amon, and Reinhardt, which correctly warned against the indiscrimi...
International audienceThis paper presents a refined single parent evolution strategy that is derando...
For algorithms of the Robbins-Monro type, the best choice (from the asymptotic point of view) for th...
Stochastic Gradient Descent (SGD) is a popular tool in training large-scale machine learning models....
International audienceStep-size adaptation for randomised search algorithms like evolution strategie...
In recent years several proposals for the step-size selection have largely improved the gradient me...
Bayesian staircases are widely used in psychophysics to estimate detection thresholds. Simulations h...
International audienceWe consider the least-squares regression problem and provide a detailed asympt...
International audienceThe CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, wher...
The study of evolutionary dynamics increasingly relies on computational methods, as more and more ca...
An influential paper by Kleibergen (2005) introduces Lagrange multiplier (LM) and conditional likeli...
International audienceA longstanding problem in sequential Monte Carlo (SMC) is to mathematically pr...