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..
Probabilistic Attention-to-Influence Neural Models for Event Sequences
Authors: Xiao Shou, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian, Oktie Hassanzadeh, Kristin Bennett
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We motivate our general framework and show improved performance in experiments compared to existing baselines on synthetic data as well as realworld benchmarks, for tasks involving prediction and influencing set identification. |
| Researcher Affiliation | Collaboration | 1Rensselaer Polytechnic Institute, Troy, NY, USA 2IBM AI Research, Yorktown Heights, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Topology-based event sequence generator (with Python pseudo code) |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the authors have released open-source code for their proposed model. It mentions a GitHub link in the appendix related to a baseline (THP), but not their own code. |
| Open Datasets | Yes | Datasets. We consider 5 real event datasets in different domains curated previously (Bhattacharjya et al., 2022). ... Diabetes (Frank & Asuncion, 2010) ... Stack Overflow (Grant & Betts, 2013) ... Linked In (Xu et al., 2017) ... Beige Books ... Timelines ... We show an example of influencing set discovery by our model Uniform-τ on a dataset derived from a corpus of news article snippets from Event Registry (Leban et al., 2014). |
| Dataset Splits | Yes | Each dataset is randomly split into 70%-15%-15% train, dev, and test set. |
| Hardware Specification | Yes | All our experiments are performed on a private server (https://idea.rpi.edu/IDEA Cluster Access) with TITAN RTX GPU. |
| Software Dependencies | No | The paper mentions implementing components in PyTorch and using the Adam optimizer, but it does not provide specific version numbers for these software dependencies (e.g., PyTorch 1.x, Adam 2.x). |
| Experiment Setup | Yes | The experiment setting for hyperparameters in Uniform-2 and Sparse-2 for binary prediction is given in Table 7. The τ values for Uniform-τ are {0.4,0.5,0.6} and for Sparse-τ they are {0.1,0.2,0.3}. |