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cs.LG2026
Loss-Driven Bayesian Active Learning
Zhuoyue Huang, Freddie Bickford Smith, Tom Rainforth
The central goal of active learning is to gather data that maximises downstream predictive performance, but popular approaches have limited flexibility in customising this data acq…
cs.LG2026
Active Learning with Task-Driven Representations for Messy Pools
Kianoosh Ashouritaklimi, Tom Rainforth
Active learning has the potential to be especially useful for messy, uncurated pools where datapoints vary in relevance to the target task. However, state-of-the-art approaches to…