arXiv Machine Learning June 25, 2026 By Daolang Huang, Zhuoyue Huang, Conor Hassan, Luigi Acerbi, Samuel Kaski, Tom Rainforth

Efficient Adaptive Data Acquisition via Pretrained Belief Representations

Read the original on arXiv Machine Learning →

arXiv:2606. 25197v1 Announce Type: new Abstract: Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecified or biased, while direct policy-learning methods map from historical observations and fail to exploit available model representations, making learning harder.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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