The minimax rate of estimating second-order calibration error is Õ(1/√n) with a matching Ω(1/√n) lower bound, enabled by analyticity from the sech kernel and yielding the first finite-sample guarantee for second-order Platt scaling.
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Bayesian active learning for classification and preferenc e learning
16 Pith papers cite this work. Polarity classification is still indexing.
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2026 16representative citing papers
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.
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
InfoChess proposes a symmetric adversarial game focused purely on information control and probabilistic king-location inference, with RL agents outperforming heuristic baselines and gameplay dissected via belief entropy, cross-entropy, and predictive scores.
LLM annotation can replace human labels for hostility detection with comparable F1 at much lower cost, but active learning adds little value and error structures differ systematically.
UG-TTT adds epistemic uncertainty measured by adapter disagreement as an exploration bonus in RL for LLMs, raising maximum reward and diversity on scientific discovery benchmarks.
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.
An ensemble-based information-theoretic active learning method using ensemble Kalman inversion selects valuable tasks to optimize communication structures in LLM multi-agent systems more reliably than random sampling under limited training budgets.
ProEval is a proactive framework using pre-trained GPs, Bayesian quadrature, and superlevel set sampling to estimate performance and find failures in generative AI with 8-65x fewer samples than baselines.
VPL learns individualized vibrotactile preferences efficiently via uncertainty-aware Gaussian process models and active query selection in a 13-participant user study on an Xbox controller.
B-ACT improves label efficiency in temporal action segmentation by selecting only boundary frames for annotation via a two-stage uncertainty-driven process that fuses neighborhood uncertainty, class ambiguity, and temporal dynamics.
HypoExplore uses LLMs for hypothesis-driven evolutionary search with a Trajectory Tree and Hypothesis Memory Bank to discover lightweight vision architectures, reaching 94.11% accuracy on CIFAR-10 from an 18.91% baseline and generalizing to other datasets including state-of-the-art on MedMNIST.
RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.
Active learning for chemical reaction extraction frequently produces non-monotonic learning curves and fails to deliver stable gains over random sampling because of strong pretraining, structured CRF decoding, and label sparsity.
Informativeness and diversity of samples selected by active learning show no correlation with test performance on translation tasks using few samples; ordering and pre-training effects dominate instead.
Feature weighting derived from ridge regression coefficients improves sample selection in pool-based sequential active learning for both single-task and multi-task regression.
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LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.