Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pairwise Causality Guided Transformers for Event Sequences
Authors: Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin P Bennett
NeurIPS 2023 | Venue PDF | 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 EMAIL Debarun Bhattacharjya IBM Research EMAIL Tian Gao IBM Research EMAIL Dharmashankar Subramanian IBM Research EMAIL Oktie Hassanzadeh IBM Research EMAIL Kristin Bennett RPI EMAIL |
| 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. |