mlsanityData to 7 Oct 2026

Bayesian Active Learning in the Presence of Nuisance Parameters

arXiv 2310.14968 ↗First posted 23 Oct 2023cs.LG stat.MLcited by 5

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Abstract (arXiv)

In many settings, such as scientific inference, optimization, and transfer learning, the learner has a well-defined objective, which can be treated as estimation of a target parameter, and no intrinsic interest in characterizing the entire data-generating process. Usually, the learner must also contend with additional sources of uncertainty or variables -- with nuisance parameters. Bayesian active learning, or sequential optimal experimental design, can straightforwardly accommodate the presence of nuisance parameters, and so is a natural active learning framework for such problems. However, the introduction of nuisance parameters can lead to bias in the Bayesian learner's estimate of the target parameters, a phenomenon we refer to as negative interference. We characterize the threat of negative interference and how it fundamentally changes the nature of the Bayesian active learner's task. We show that the extent of negative interference can be extremely large, and that accurate estimation of the nuisance parameters is critical to reducing it. The Bayesian active learner is confronted with a dilemma: whether to spend a finite acquisition budget in pursuit of estimation of the target or of the nuisance parameters. Our setting encompasses Bayesian transfer learning as a special case, and our results shed light on the phenomenon of negative transfer between learning environments.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 29 of its 47 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

29 of 47 references.

References · 29

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17 more
  1. 2018Adversarially Robust Optimization with Gaussian Processescited by 61
  2. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  3. 2019A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experimentscited by 58
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2025Generalization Bounds and Stopping Rules for Learning with Self-Selected Datacited by 5
  2. 2024Proxy-informed Bayesian transfer learning with unknown sourcescited by 2
  3. 2025Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecificationcited by 1
  4. 2026Diagnose Before You Compress: Prediction-Independent Bottleneck Witness Refinement for LLM Serving Tracescited by 1
  5. 2026Learning What Matters: Adaptive Information-Theoretic Objectives for Robot Explorationcited by 0