FairBED quantifies dataset fairness as uninformative about sensitive attributes and uses fairness-aware BED to gather data yielding better fairness-accuracy trade-offs than random or standard BED acquisition.
Policy-based bayesian experimental design for non-differentiable implicit models.arXiv preprint arXiv:2203.04272,
3 Pith papers cite this work. Polarity classification is still indexing.
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POLAR uses pretrained predictive foundation models as fixed belief-state encoders and trains only a lightweight policy head on top for amortised Bayesian experimental design, optimisation, and active learning.
Combines offline amortized pre-training with online scenario-tree planning to optimize constrained Bayesian experimental designs, producing more informative sequences than prior methods.
citing papers explorer
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FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data
FairBED quantifies dataset fairness as uninformative about sensitive attributes and uses fairness-aware BED to gather data yielding better fairness-accuracy trade-offs than random or standard BED acquisition.
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Efficient Adaptive Data Acquisition via Pretrained Belief Representations
POLAR uses pretrained predictive foundation models as fixed belief-state encoders and trains only a lightweight policy head on top for amortised Bayesian experimental design, optimisation, and active learning.
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Constrained Bayesian Experimental Design via Online Planning
Combines offline amortized pre-training with online scenario-tree planning to optimize constrained Bayesian experimental designs, producing more informative sequences than prior methods.