Observation-dependent Bayesian active learning via input-warped Gaussian processes
This work addresses a bottleneck in active learning for researchers and practitioners by making exploration more responsive to data, though it is an incremental improvement over existing methods.
The paper tackled the problem of Bayesian active learning being insensitive to observed outputs by proposing an input-warped Gaussian process that adapts the input space based on measurements, resulting in improved sample efficiency across benchmarks, with specific gains in non-stationary regimes.
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.