Pairwise Causality Guided Transformers for Event Sequences
Authors: Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P Bennett
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approach outperforms several state-of-the-art models in terms of prediction accuracy by effectively leveraging knowledge about causal pairs. ... We conduct a detailed empirical evaluation demonstrating superior predictive performance as compared to state-of-the-art baselines on synthetic datasets as well as real-world benchmarks. |
| Researcher Affiliation | Collaboration | Xiao Shou RPI xshou01@gmail.com Debarun Bhattacharjya IBM Research debarunb@us.ibm.com Tian Gao IBM Research tgao@us.ibm.com Dharmashankar Subramanian IBM Research dharmash@us.ibm.com Oktie Hassanzadeh IBM Research hassanzadeh@us.ibm.com Kristin Bennett RPI bennek@rpi.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Diabetes contains meals, exercise activity, insulin dosage, and changes in blood glucose measurements for a group of 65 diabetes patients [53]. ... [53] Andrew Frank and Arthur Asuncion. UCI machine learning repository, 2010. |
| Dataset Splits | Yes | We run experiments on 4 generated synthetic datasets to verify the learning capabilities and validity of our approach. ... each dataset was split into train/dev/test sets (60/20/20)%. ... All datasets were split into train-dev-test sets (60/20/20)% for fair evaluation, where hyper-parameters were chosen using train-dev sets and evaluation was performed on the test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (such as exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a 'transformer architecture' and a 'flan-t5-xxl(11B)' model but does not provide specific software dependencies with version numbers (e.g., library or framework versions, Python version) needed to replicate the experiment. |
| Experiment Setup | No | Further details around implementation and training can be found in the Appendix. ... Further details such as hyper-parameters chosen based on the validation set are discussed in the Appendix. |