# Decision Rule Elicitation for Domain Adaptation

arXiv: https://arxiv.org/abs/2102.11539 · HTML: https://mlsanity.com/paper/2102.11539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 23 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation](https://mlsanity.com/paper/1705.03290/) (2017)
- [Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction](https://mlsanity.com/paper/1612.03328/) (2016)
- [Interactive Prior Elicitation of Feature Similarities for Small Sample Size Prediction](https://mlsanity.com/paper/1612.02802/) (2016)

## Built on by (most cited first, 1 of 1)

- [Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations](https://mlsanity.com/paper/2206.11574/) (2022)

## Abstract (arXiv)

Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels for data points from experts or to provide feedback on how close the predicted results are to the target. This simplifies away all the details of the decision-making process of the expert. In this work, we allow the experts to additionally produce decision rules describing their decision-making; the rules are expected to be imperfect but to give additional information. In particular, the rules can extend to new distributions, and hence enable significantly improving performance for cases where the training and testing distributions differ, such as in domain adaptation. We apply the proposed method to lifelong learning and domain adaptation problems and discuss applications in other branches of AI, such as knowledge acquisition problems in expert systems. In simulated and real-user studies, we show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.