Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry

30 September 2020

Papers citing "Metrics for Benchmarking and Uncertainty Quantification: Quality, Applicability, and a Path to Best Practices for Machine Learning in Chemistry"

8 / 8 papers shown

Title
Learning signals defined on graphs with optimal transport and Gaussian process regression Raphael Carpintero Perez Sébastien da Veiga Josselin Garnier B. Staber 36 1 0 21 Oct 2024
On Fixing the Right Problems in Predictive Analytics: AUC Is Not the Problem Ryan S. Baker Nigel Bosch Stephen Hutt A. F. Zambrano Alex J. Bowers CML 24 0 0 10 Apr 2024
Best practices for machine learning in antibody discovery and development Leonard Wossnig Norbert Furtmann Andrew Buchanan Sandeep Kumar Victor Greiff 16 7 0 13 Dec 2023
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity Pascal Pernot 25 9 0 12 Sep 2023
On minimizing the training set fill distance in machine learning regression Paolo Climaco Jochen Garcke 10 1 0 20 Jul 2023
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation Diego Martinez-Taboada Dino Sejdinovic CML OffRL 19 0 0 02 Nov 2022
A view on model misspecification in uncertainty quantification Yuko Kato David Tax Marco Loog 23 3 0 30 Oct 2022
Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity Nikhil V. S. Avula S. K. Veesam Sudarshan Behera S. Balasubramanian 24 8 0 23 Aug 2022