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..
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Authors: Amir Feder, Guy Horowitz, Yoav Wald, Roi Reichart, Nir Rosenfeld
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in multiple settings demonstrate the effectiveness of our approach. |
| Researcher Affiliation | Academia | Amir Feder Columbia University EMAIL Guy Horowitz Technion Yoav Wald Johns Hopkins University Roi Reichart Technion Nir Rosenfeld Technion |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and Data for reproducing all of our results are submitted as part of the supplementary material. |
| Open Datasets | Yes | We use Rate Beer, a dataset of beer reviews with over 3M entries and spanning 10 years [32]. We use the fashion product images dataset, which includes includes 44.4πfashion items described by images, attributes, and text. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5 and Supplementary material |
| Hardware Specification | Yes | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] See supplementary material. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers in the main text. |
| Experiment Setup | Yes | We consider each year as an environment π, with each πinducing a distribution over (π’, π₯, π). ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5 and Supplementary material |