GRINCO performs acquisition in the quotient space induced by a transformation group using invariant embeddings or canonical representatives, pairs it with orbit-averaged loss, derives a generalization bound, and reports better orbit coverage and label efficiency than standard coresets on synthetic a
A coreset selection of coreset selection literature: Introduction and recent advances
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5representative citing papers
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
SAS adds semantic scoring with CLIP and a two-stage filter-then-diversity selection process to make generative dataset distillation produce more class-discriminative and diverse compact datasets.
Selecting per-class medoids (samples with lowest average L2 distance to all same-class samples in teacher feature space) consistently outperforms random, herding, and k-center Greedy baselines for few-shot knowledge distillation on from-scratch students.
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.
citing papers explorer
-
Group-invariant Coresets for Data-efficient Active Learning
GRINCO performs acquisition in the quotient space induced by a transformation group using invariant embeddings or canonical representatives, pairs it with orbit-averaged loss, derives a generalization bound, and reports better orbit coverage and label efficiency than standard coresets on synthetic a
-
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
SKMD adapts Stein variational gradient descent into molecular dynamics with asynchronous updates and global atomic descriptor kernels to acquire non-redundant training configurations while preserving the Boltzmann distribution, yielding higher MLIP accuracy with fewer samples than baselines.
-
SAS: Semantic-aware Sampling for Generative Dataset Distillation
SAS adds semantic scoring with CLIP and a two-stage filter-then-diversity selection process to make generative dataset distillation produce more class-discriminative and diverse compact datasets.
-
Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
Selecting per-class medoids (samples with lowest average L2 distance to all same-class samples in teacher feature space) consistently outperforms random, herding, and k-center Greedy baselines for few-shot knowledge distillation on from-scratch students.
-
Efficient Benchmarking Is Just Feature Selection and Multiple Regression
Kernel ridge regression combined with mRMR feature selection improves prediction of full benchmark scores from question subsets over existing efficient benchmarking techniques.