Garth Weels

Artificial Intelligence · 2014

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Garth Weels trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Phillip Boyle (in Artificial Intelligence)
  2. Sébastien Le Digabel (in Artificial Intelligence)
  3. Dimitri P. Bertsekas (in Optimization & Training)

Papers from Garth Weels's perspective

Ordered by how strongly the researchers Garth Weels trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Design and analysis of computer experiments
  2. A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
  3. Global versus local search in constrained optimization of computer models
  4. Spatial modelling using a new class of nonstationary covariance functions (Environmetrics)
  5. Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations.
  6. Mesh Adaptive Direct Search Algorithms for Constrained Optimization
  7. Convergence Rates of Efficient Global Optimization Algorithms
  8. Constrained Optimization and Lagrange Multiplier Methods
  9. A surrogate-model-based method for constrained optimization
  10. Bayesian Treed Gaussian Process Models With an Application to Computer Modeling

Their papers