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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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.
The generalization advantage of SGD over random sampling diminishes with growing training set size in binary networks, as measured by joint density of states over train and test accuracy.
SeqLoRA applies bilevel optimization to sequential LoRA adaptation for continual multi-concept text-to-image generation with theoretical bounds on forgetting and interference.
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.
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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Revisiting the Volume Hypothesis
The generalization advantage of SGD over random sampling diminishes with growing training set size in binary networks, as measured by joint density of states over train and test accuracy.
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SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
SeqLoRA applies bilevel optimization to sequential LoRA adaptation for continual multi-concept text-to-image generation with theoretical bounds on forgetting and interference.
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Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
Predicting question-level rectification difficulty from text and allocating human labels by a square-root rule recovers most of the hybrid human–LLM survey efficiency gains without pilot data.
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REX-SUB: A Scalable Subsampling Strategy for Modeling Large Spatial Datasets
REX-SUB combines a randomized exchange algorithm with Vecchia approximation to choose subsamples that minimize mean squared prediction error and interval scores in large-scale spatial GPs.