Bayesian active learning for classification and preference learning.arXiv preprint arXiv:1112.5745

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• The Minimax Rate of Second-Order Calibration cs.LG · 2026-05-08 · unverdicted · none · ref 7

  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.

• Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights cs.LG · 2026-05-11 · unverdicted · none · ref 18

  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.

• LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification cs.AI · 2026-05-08 · conditional · none · ref 30

  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: A Game of Adversarial Inference and a Laboratory for Quantifiable Information Control cs.MA · 2026-04-15 · unverdicted · none · ref 10

  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.

• Do We Still Need Humans in the Loop? Comparing Human and LLM Annotation in Active Learning for Hostility Detection cs.CL · 2026-04-15 · conditional · none · ref 4

  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.

• Epistemic Uncertainty for Test-Time Discovery cs.LG · 2026-05-11 · unverdicted · none · ref 13

  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: Identifiable Epistemic-Aleatoric Decomposition via Structured Synthetic Priors stat.ML · 2026-05-07 · conditional · none · ref 2

  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.

• Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cs.MA · 2026-05-07 · unverdicted · none · ref 29 · 2 links

  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: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation cs.LG · 2026-04-25 · unverdicted · none · ref 27

  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.

• Vibrotactile Preference Learning: Uncertainty-Aware Preference Learning for Personalized Vibration Feedback cs.HC · 2026-04-22 · unverdicted · none · ref 17

  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.

• Boundary-Centric Active Learning for Temporal Action Segmentation cs.CV · 2026-04-16 · unverdicted · none · ref 56

  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.

• Agentic Discovery with Active Hypothesis Exploration for Visual Recognition cs.CV · 2026-04-14 · unverdicted · none · ref 19

  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: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settings cs.CL · 2026-04-22 · unverdicted · none · ref 62

  RADS applies reinforcement learning to pick informative samples for transfer learning, improving performance over uncertainty and diversity sampling in low-resource imbalanced clinical settings.

• When Active Learning Falls Short: An Empirical Study on Chemical Reaction Extraction cs.LG · 2026-04-21 · unverdicted · none · ref 46

  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.

• Testing the Assumptions of Active Learning for Translation Tasks with Few Samples cs.CL · 2026-04-10 · unverdicted · none · ref 10

  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 Improves Pool-Based Sequential Active Learning for Regression cs.LG · 2026-04-02 · conditional · none · ref 28

  Feature weighting derived from ridge regression coefficients improves sample selection in pool-based sequential active learning for both single-task and multi-task regression.