Greedy myopic Bayesian active learning for linear regression achieves risk within a factor linear in the maximum initial leverage score of optimal, and this factor is tight.
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3 Pith papers cite this work. Polarity classification is still indexing.
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AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
Decoupled PFNs use controllable synthetic priors to train separate latent-signal and noise heads, making epistemic-aleatoric decomposition identifiable and improving acquisition in noisy settings.
citing papers explorer
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The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Greedy myopic Bayesian active learning for linear regression achieves risk within a factor linear in the maximum initial leverage score of optimal, and this factor is tight.
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Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
AB-SID-iVAR enables Gaussian process active learning for self-induced Boltzmann distributions by closed-form approximation of the target, with high-probability error vanishing guarantees and empirical gains on PES and drug discovery tasks.
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Decoupled PFNs: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors
Decoupled PFNs use controllable synthetic priors to train separate latent-signal and noise heads, making epistemic-aleatoric decomposition identifiable and improving acquisition in noisy settings.