Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability

18 October 2024

Papers citing "Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability"

10 / 10 papers shown

Title
Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables Manjinder Singh Alexander E. I. Brownlee David Cairns 47 13 0 31 May 2022
Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020 Ryan Turner David Eriksson M. McCourt J. Kiili Eero Laaksonen Zhen Xu Isabelle M Guyon BDL 60 302 0 20 Apr 2021
Population-Based Black-Box Optimization for Biological Sequence Design Christof Angermüller David Belanger Andreea Gane Zelda E. Mariet David Dohan Kevin Patrick Murphy Lucy J. Colwell D. Sculley 72 133 0 05 Jun 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 121 6,269 0 22 Oct 2019
A Tutorial on Bayesian Optimization P. Frazier GP 109 1,788 0 08 Jul 2018
The reparameterization trick for acquisition functions James T. Wilson Riccardo Moriconi Frank Hutter M. Deisenroth 63 81 0 01 Dec 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,939 0 22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
Determinantal point processes for machine learning Alex Kulesza B. Taskar 252 1,140 0 25 Jul 2012
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization Thomas Desautels Andreas Krause J. W. Burdick 104 473 0 27 Jun 2012