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 | Conference PDF | Archive PDF | Plain Text | 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 amir.feder@columbia.edu 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 44.4𝑘fashion 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 𝑒, with each 𝑒inducing a distribution over (𝑢, 𝑥, 𝑟). ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Section 5 and Supplementary material |