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Summary of Heteroscedastic Preferential Bayesian Optimization with Informative Noise Distributions, by Marshal Arijona Sinaga and Julien Martinelli and Vikas Garg and Samuel Kaski


Heteroscedastic Preferential Bayesian Optimization with Informative Noise Distributions

by Marshal Arijona Sinaga, Julien Martinelli, Vikas Garg, Samuel Kaski

First submitted to arxiv on: 23 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: 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
This paper proposes Preferential Bayesian Optimization (PBO) with heteroscedastic noise models to capture varying levels of human aleatoric uncertainty when evaluating candidate designs. Classical PBO relies on homoscedastic noise models, which fail to account for users’ partial knowledge in comparing different pairs of candidates. The proposed approach uses anchors, predefined reliable inputs, to adaptively assign noise levels based on the distance from the input to the anchors. This model can be integrated into the acquisition function, leading to more informative and comparable candidate design pairs for human experts. The authors demonstrate a consistent improvement over homoscedastic PBO through extensive empirical evaluation.
Low GrooveSquid.com (original content) Low Difficulty Summary
PBO is a way to find better designs by asking people which ones they like best. Right now, it doesn’t do a great job of understanding how hard or easy it is for people to compare different designs. This paper tries to fix that by adding some extra information about what people already know about certain designs. It uses this information to make sure the suggestions are good and easy to understand. The authors tested their idea and showed that it works better than the old way.

Keywords

» Artificial intelligence  » Optimization