dogear

enter for all results · esc to close

Practical Bayesian Optimization of Machine Learning Algorithms

proceedings.neurips.ccsite

NeurIPS'12, 2012. [All Versions]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force…

from
AGI & CoCoSci
added
2026-10-10
likes
0

similar

  1. Tutorial on Bayesian Optimisation for Machine Learning iro.umontreal.ca
  2. The Interplay of Optimization and Machine Learning Research jmlr.org
  3. Hyperparameter-Optimization-of-Machine-Learning-Algorithms github.com
  4. When to Trust Your Model: Model-Based Policy Optimization proceedings.neurips.cc

    NeurIPS'19, 2019. [All Versions]. [Post].

  5. A Tutorial on Bayesian Optimization arxiv.org

    2018. [All Versions]. Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function…

  6. Optimization Algorithms in Machine Learning pages.cs.wisc.edu

    , Video Lecture

AGI & CoCoSci › Papers > Bayesian Modeling > Bayesian Optimization: “NeurIPS'12, 2012. [All Versions]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force…”