2022
NIPS
NeurIPS 2022
Active Labeling: Streaming Stochastic Gradients
Abstract
The workhorse of machine learning is stochastic gradient descent.To access stochastic gradients, it is common to consider iteratively input/output pairs of a training dataset.Interestingly, it appears that one does not need full supervision to access stochastic gradients, which is the main motivation of this paper.After formalizing the "active labeling" problem, which focuses on active learning with partial supervision, we provide a streaming technique that provably minimizes the ratio of generalization error over the number of samples.We illustrate our technique in depth for robust regression.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Mathematics & Optimization
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Machine Learning > Learning Types > Active Learning
Machine Learning > Optimization & Theory > Stochastic Processes
Mathematics & Optimization > Optimization > Online Algorithms
Deep Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Paradigms > Active Learning
Machine Learning > Learning Types > Stochastic Methods