mlsanityData to 14 Oct 2026

Controllable Missingness from Uncontrollable Missingness: Joint Learning Measurement Policy and Imputation

arXiv 2204.03872 ↗First posted 8 Apr 2022cs.LG cs.CVcited by 0

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Abstract (arXiv)

Due to the cost or interference of measurement, we need to control measurement system. Assuming that each variable can be measured sequentially, there exists optimal policy choosing next measurement for the former observations. Though optimal measurement policy is actually dependent on the goal of measurement, we mainly focus on retrieving complete data, so called as imputation. Also, we adapt the imputation method to missingness varying with measurement policy. However, learning measurement policy and imputation requires complete data which is impossible to be observed, unfortunately. To tackle this problem, we propose a data generation method and joint learning algorithm. The main idea is that 1) the data generation method is inherited by imputation method, and 2) the adaptation of imputation encourages measurement policy to learn more than individual learning. We implemented some variations of proposed algorithm for two different datasets and various missing rates. From the experimental results, we demonstrate that our algorithm is generally applicable and outperforms baseline methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 38 references that are in the map. First posted on arXiv 8 Apr 2022.

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10 of 38 references.

References · 10

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  3. 2016Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoisingcited by 1,358
  4. 2015End-to-End Training of Deep Visuomotor Policiescited by 875
  5. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 352
  6. 2018An Introduction to Deep Reinforcement Learningcited by 206
  7. 2017Virtual to Real Reinforcement Learning for Autonomous Drivingcited by 80
  8. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 75
  9. 2020Learning When and Where to Zoom with Deep Reinforcement Learningcited by 43
  10. 2017Flexible High-Dimensional Unsupervised Learning with Missing Datacited by 3

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