(2022) Discovering Interpretable Data-to-Sequence Generators.
|
Text
consequence-wiegand,klakow,vreeken-preprint-wapx.pdf - Draft Version Download (514kB) | Preview |
Abstract
We study the problem of predicting an event sequence given some meta data. In particular, we are interested in learning easily interpretable models that can accurately generate a se- quence based on an attribute vector. To this end, we propose to learn a sparse event-flow graph over the training sequences, and statistically robust rules that use meta data to determine which paths to follow. We formalize the problem in terms of the Minimum Description Length (MDL) principle, by which we identify the best model as the one that compresses the data best. As the resulting optimization problem is NP-hard, we propose the efficient CONSEQUENCE algorithm to discover good event-flow graphs from data. Through an extensive set of experiments including a case study, we show that it ably discovers compact, interpretable and accurate models for the generation and prediction of event sequences from data, has a low sample complexity, and is particularly robust against noise.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Jilles Vreeken (Exploratory Data Analysis) |
| Conference: | AAAI National Conference of the American Association for Artificial Intelligence |
| Depositing User: | Sebastian Dalleiger |
| Date Deposited: | 17 Dec 2021 10:45 |
| Last Modified: | 17 Dec 2021 10:45 |
| Primary Research Area: | NRA1: Trustworthy Information Processing |
| URI: | https://publications.cispa.saarland/id/eprint/3557 |
Actions
Actions (login required)
 |
View Item |