3 papers
cs.LG2026
Pool-based Active Learning as Noisy Lossy Compression: Characterizing Label Complexity via Finite Blocklength Analysis
Kosuke Sugiyama, Masato Uchida
This paper proposes an information-theoretic framework for analyzing the theoretical limits of pool-based active learning (AL), in which a subset of instances is selectively labele…
cs.LG2025
Learning from Hard Labels with Additional Supervision on Non-Hard-Labeled Classes
Kosuke Sugiyama, Masato Uchida
In scenarios where training data is limited due to observation costs or data scarcity, enriching the label information associated with each instance becomes crucial for building hi…
cs.LG2024
Learning from Complementary Features
Kosuke Sugiyama, Masato Uchida
While precise data observation is essential for the learning processes of predictive models, it can be challenging owing to factors such as insufficient observation accuracy, high…