Related papers: Observation-dependent Bayesian active learning via input-warped Gaussian processes

Observation-dependent Bayesian active learning via input-warped Gaussian processes

URL: http://arxiv.org/abs/2602.01898v1
Date: Mon, 02 Feb 2026 10:05:56 GMT
Title: Observation-dependent Bayesian active learning via input-warped Gaussian processes
Authors: Sanna Jarl, Maria Bånkestad, Jonathan J. S. Scragg, Jens Sjölund,
Abstract summary: We propose to inject observation-dependent feedback by warping the input space with a learned, monotone re parameterization.<n>This mechanism allows the design policy to expand or compress regions of the input space in response to observed variability.<n>We demonstrate that while such warps can be trained via marginal likelihood, a novel self-supervised objective yields substantially better performance.
Score: 4.519209749095341
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Bayesian active learning relies on the precise quantification of predictive uncertainty to explore unknown function landscapes. While Gaussian process surrogates are the standard for such tasks, an underappreciated fact is that their posterior variance depends on the observed outputs only through the hyperparameters, rendering exploration largely insensitive to the actual measurements. We propose to inject observation-dependent feedback by warping the input space with a learned, monotone reparameterization. This mechanism allows the design policy to expand or compress regions of the input space in response to observed variability, thereby shaping the behavior of variance-based acquisition functions. We demonstrate that while such warps can be trained via marginal likelihood, a novel self-supervised objective yields substantially better performance. Our approach improves sample efficiency across a range of active learning benchmarks, particularly in regimes where non-stationarity challenges traditional methods.

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