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Found 9 papers, 3 papers with code
Learning from the Best: Active Learning for Wireless Communications
We evaluate the performance of different active learning algorithms on a publicly available multi-modal dataset with different modalities including image and LiDAR.
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
To mitigate the annotation cost of SFT and circumvent the computational bottlenecks of active learning, we propose using experimental design.
DIRECT: Deep Active Learning under Imbalance and Label Noise
Our results demonstrate that DIRECT can save more than 60% of the annotation budget compared to state-of-art active learning algorithms and more than 80% of annotation budget compared to random sampling.
LabelBench: A Comprehensive Framework for Benchmarking Adaptive Label-Efficient Learning
Labeled data are critical to modern machine learning applications, but obtaining labels can be expensive.
Algorithm Selection for Deep Active Learning with Imbalanced Datasets
To address this, we propose the first adaptive algorithm selection strategy for deep active learning.
PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks
Weight decay is one of the most widely used forms of regularization in deep learning, and has been shown to improve generalization and robustness.
GALAXY: Graph-based Active Learning at the Extreme
Active learning is a label-efficient approach to train highly effective models while interactively selecting only small subsets of unlabelled data for labelling and training.
Improved Algorithms for Agnostic Pool-based Active Classification
We consider active learning for binary classification in the agnostic pool-based setting.
Learning to Actively Learn: A Robust Approach
Unlike the design of traditional adaptive algorithms that rely on concentration of measure and careful analysis to justify the correctness and sample complexity of the procedure, our adaptive algorithm is learned via adversarial training over equivalence classes of problems derived from information theoretic lower bounds.
