KCas transfers student-selected smoothing parameters to full-sample teacher models via asymptotic scaling laws in smoothing splines and kernel methods, cutting computation while retaining performance guarantees.
Dataset pruning: Reducing training data by examining generalization influence.arXiv preprint arXiv:2205.09329
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
representative citing papers
Introduces the first active learning framework for unaligned multimodal data that selects alignments using uncertainty and diversity to cut annotation costs by up to 40% on benchmarks while preserving accuracy.
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
Electronic structure datasets across materials show high redundancy from low intrinsic dimensionality, allowing pruning to 1/100th size with preserved chemical accuracy.
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
OrderDP is a plug-and-play data pruning method that selects a random subset then top-q samples to guarantee unbiased surrogate-loss training with convergence analysis and over 40% training cost reduction on CIFAR and ImageNet.
SLAP is a new batch-aware pruning framework that uses distribution-aware stratified sampling and Hessian-approximated gradients to select data, claiming 20-40% less data while matching or exceeding full-dataset performance on LLM instruction tuning tasks.
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.
citing papers explorer
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Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation
KCas transfers student-selected smoothing parameters to full-sample teacher models via asymptotic scaling laws in smoothing splines and kernel methods, cutting computation while retaining performance guarantees.
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Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data
Introduces the first active learning framework for unaligned multimodal data that selects alignments using uncertainty and diversity to cut annotation costs by up to 40% on benchmarks while preserving accuracy.
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Representation-Guided Parameter-Efficient LLM Unlearning
REGLU guides LoRA-based unlearning via representation subspaces and orthogonal regularization to outperform prior methods on forget-retain trade-off in LLM benchmarks.
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Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality
Electronic structure datasets across materials show high redundancy from low intrinsic dimensionality, allowing pruning to 1/100th size with preserved chemical accuracy.
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SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
SalUn uses gradient-based weight saliency to achieve effective machine unlearning of data, classes, or concepts in image classification and generation, narrowing the gap to exact retraining.
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OrderDP: A Theoretically Guaranteed Lossless Dynamic Data Pruning Framework
OrderDP is a plug-and-play data pruning method that selects a random subset then top-q samples to guarantee unbiased surrogate-loss training with convergence analysis and over 40% training cost reduction on CIFAR and ImageNet.
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SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
SLAP is a new batch-aware pruning framework that uses distribution-aware stratified sampling and Hessian-approximated gradients to select data, claiming 20-40% less data while matching or exceeding full-dataset performance on LLM instruction tuning tasks.
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Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks
Adaptive Data Dropout uses performance feedback to dynamically modulate training data exposure, reducing effective steps while matching static dropout accuracy on image benchmarks.