Summary of Distribution-free Conformal Joint Prediction Regions For Neural Marked Temporal Point Processes, by Victor Dheur and Tanguy Bosser and Rafael Izbicki and Souhaib Ben Taieb
Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes
by Victor Dheur, Tanguy Bosser, Rafael Izbicki, Souhaib Ben Taieb
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper develops novel methods for uncertainty quantification in neural Temporal Point Process (TPP) models using conformal prediction techniques. Neural TPPs are widely used to model sequences of labeled events observed at irregular intervals in continuous time, enabling predictions such as the arrival time and mark of future events. However, existing models may provide poor approximations of the true underlying process, leading to unreliable uncertainty estimates. The authors aim to generate distribution-free joint prediction regions for event arrival times and marks with a finite-sample marginal coverage guarantee. They propose two approaches: combining individual prediction regions and using bivariate highest density regions derived from the joint predictive density. Additionally, they explore conformal regression and classification techniques for generating univariate prediction regions. The paper’s efficacy is evaluated on simulated and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve predictions in Temporal Point Process (TPP) models by better understanding uncertainty. It uses a special technique called conformal prediction to create more reliable estimates of when events will happen and what they might be about. Right now, these models can be pretty bad at predicting things because they don’t fully understand the data. The authors want to change that by creating new ways to look at the data and make predictions. They propose two main methods: combining separate predictions for arrival times and marks, or using special regions that show where the most likely events will happen. They also test these ideas on real-world data to see how well they work. |
Keywords
* Artificial intelligence * Classification * Regression
