Summary of Adaptive Conformal Inference by Particle Filtering Under Hidden Markov Models, By Xiaoyi Su et al.
Adaptive Conformal Inference by Particle Filtering under Hidden Markov Models
by Xiaoyi Su, Zhixin Zhou, Rui Luo
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed adaptive conformal inference framework leverages particle filtering to address the challenge of conducting conformal inference for hidden states under hidden Markov models (HMMs). The framework uses weighted particles as an approximation of the actual posterior distribution of the hidden state, allowing for online adaptation to time-varying data distributions. The goal is to produce prediction sets that encompass these particles to achieve a specific aggregate weight sum, referred to as the aggregated coverage level. This approach is verified through a real-time target localization simulation study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure how likely our predictions are correct by using a special kind of machine learning called conformal inference. It’s hard to use this method when we don’t have all the information, but they found a way to make it work by pretending that we do know some things. They used something called particle filtering to make their predictions more accurate and reliable. This new approach can be used in many different situations where we need to predict what might happen next. |
Keywords
* Artificial intelligence * Inference * Machine learning
