Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric
Authors: Nirandika Wanigasekara, Christina Yu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide regret bounds along with simulations that highlight the algorithm s dependence on the local geometry of the reward functions. We provide simulations that compare our algorithm to oracle variants that have special knowledge of the arms and a naive benchmark that learns over each arm separately. |
| Researcher Affiliation | Academia | Nirandika Wanigasekara Computer Science National University of Singapore nirandiw@comp.nus.edu.sg Christina Lee Yu Operations Research and Information Engineering Cornell University cleeyu@cornell.edu |
| Pseudocode | Yes | See the appendix for a pseudocode description of the algorithm. |
| Open Source Code | No | The paper states 'See the appendix for a pseudocode description of the algorithm.' but does not provide concrete access (e.g., a link to a repository) to the source code for the described methodology, nor an explicit statement about its open-source availability. |
| Open Datasets | No | The paper uses a synthetic model to generate data for simulations, describing how contexts and arms are sampled ('context xt U(0, 1)', 'Each arm a corresponds to a parameter θa uniformly spaced out within [0, 1]'). It does not refer to or provide access information for a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes an online learning setting with trials over a time horizon and a simulation setup, but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | For all algorithms the flagging rule is set to nt(ρ) 4 ln(T)/ 2, and σ was set to 1e 2. For Approx-Zooming , k was set to 10. We set the number of trials T to 100, 000 as all the algorithms had converged to their optimal point by then. Additional details on how the model parameters were chosen is given in Appendix F. |