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..
Cascading Contextual Assortment Bandits
Authors: Hyun-jun Choi, Rajan Udwani, Min-hwan Oh
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We substantiate our theoretical claims with numerical experiments, demonstrating the practical efficacy of our proposed methods. |
| Researcher Affiliation | Academia | Hyun-jun Choi Seoul National University Rajan Udwani UC Berkeley Min-hwan Oh Seoul National University |
| Pseudocode | Yes | Algorithm 1 UCB-CCA |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes generating synthetic data for simulations rather than using a publicly available dataset. It states: 'For simulations, we generate a random sample of the unknown time-invariant parameter from N(0, 1) at the beginning of the simulation. We sample N feature vectors from N(0, 1) in each round t.' |
| Dataset Splits | No | The paper focuses on online learning and regret in a simulation setting with synthetically generated data, and thus does not explicitly describe train/validation/test dataset splits or cross-validation methodology. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general simulation setup, including data generation parameters (e.g., sampling from N(0,1)) and how the oracle computes cascades, but it does not specify concrete hyperparameters or system-level training configurations (e.g., specific values for the ridge penalty parameter λ used in experiments, learning rates, or other optimizer settings). |