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2010 AISTATS AISTATS 2010

Negative Results for Active Learning with Convex Losses

Abstract

We study the problem of active learning with convex loss functions. We prove that even under bounded noise constraints, the minimax rates for proper active learning are often no better than passive learning.

🚀 Conference Pioneer — AISTATS 2010
🧭 Keyword Pioneer — proper learning
🐣 Hot Topic Early Bird — active learning
🐝 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

Steve Hanneke , Liu Yang

Topics

Machine Learning > Learning Types > Active Learning Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Learning Types > Supervised Learning

Keywords

active learning convex loss passive learning minimax rate proper learning
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