Summary Markov Models for Event Sequences
Authors: Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh, Liza Bialik
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an experimental investigation comparing the proposed models with relevant baselines, and illustrate their knowledge acquisition and discovery capabilities through case studies involving sequences from text. ... 5 Experiments The following experiments assess our two proposed Su MMs as well as our learning approach. |
| Researcher Affiliation | Collaboration | Debarun Bhattacharjya1 , Saurabh Sihag2 , Oktie Hassanzadeh1 and Liza Bialik3 1IBM Research 2University of Pennsylvania 3University of Massachusetts, Amherst |
| Pseudocode | Yes | Algorithm 1 Greedy Score-based Search and procedure COMPUTESCORE are presented in Section 4. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a direct link to a code repository for the methodology described. |
| Open Datasets | Yes | Datasets. We consider the following structured datasets, some derived from time-stamped event datasets where the time stamp is ignored (assumed to be missing or erroneous). Diabetes [Frank and Asuncion, 2010]: Events for diabetic patients around meal ingestion, exercise, insulin intake and blood glucose level transitions b/w low, medium and high. Linked In [Xu et al., 2017]: Employment related events such as joining a new role for 1000 Linked In users. Stack Overflow [Grant and Betts, 2013]: Events for engagement of 1000 users (chosen from [Du et al., 2016]) around receipt of badges in a question answering website. |
| Dataset Splits | Yes | For experiments, each dataset is split into train/dev/test sets (70/15/15)%, removing labels that are not common across all three splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions) needed to replicate the experiment. |
| Experiment Setup | Yes | For experiments, we use a single parameter α as hyper-parameter, assuming that αx|s = α x, s. ... BIC score penalizes models that are overly complex... where γ is a penalty on complexity, generally set at a default value of 1 unless otherwise specified. |