Summary of Ranking Over Regression For Bayesian Optimization and Molecule Selection, by Gary Tom et al.
Ranking over Regression for Bayesian Optimization and Molecule Selection
by Gary Tom, Stanley Lo, Samantha Corapi, Alan Aspuru-Guzik, Benjamin Sanchez-Lengeling
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Bayesian optimization (BO) has become a crucial tool in autonomous decision-making across various applications, from self-driving vehicles to accelerated drug and materials discovery. The paper introduces Rank-based Bayesian Optimization (RBO), which uses ranking models as surrogates instead of regression models. RBO is compared to conventional BO on chemical datasets, showing similar or improved optimization performance. The results highlight the effectiveness of RBO in optimizing novel chemical compounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Bayesian optimization helps machines make decisions alone. This paper shows how to make it better by using a new type of model that ranks things instead of predicting exact values. They test this new way on special datasets about chemicals and find that it works just as well or even better than the old way. This is useful for finding new medicines and materials. |
Keywords
» Artificial intelligence » Optimization » Regression
