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Found 7 papers, 2 papers with code
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.
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.
apricot: Submodular selection for data summarization in Python
This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets.
Knows When it Doesn’t Know: Deep Abstaining Classifiers
We introduce the deep abstaining classifier -- a deep neural network trained with a novel loss function that provides an abstention option during training.
Deep Submodular Functions
Lastly, we discuss strategies to learn DSFs, and define the classes of deep supermodular functions, deep difference of submodular functions, and deep multivariate submodular functions, and discuss where these can be useful in applications.
Semi-Supervised Phone Classification using Deep Neural Networks and Stochastic Graph-Based Entropic Regularization
We describe a graph-based semi-supervised learning framework in the context of deep neural networks that uses a graph-based entropic regularizer to favor smooth solutions over a graph induced by the data.
Efficient Distributed Semi-Supervised Learning using Stochastic Regularization over Affinity Graphs
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting.
