Cascading Contextual Assortment Bandits

Authors: Hyun-jun Choi, Rajan Udwani, Min-hwan Oh

NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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).