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..
Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context
Authors: Osama Hanna, Lin Yang, Christina Fragouli
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]. Also, from the abstract: "achieving nearly the same regret bound as if the contexts were directly observable. The former bound improves upon existing bounds by a log(T) factor, while the latter achieves information theoretical tightness." |
| Researcher Affiliation | Academia | Osama A. Hanna University of California, Los Angeles EMAIL Lin F. Yang University of California, Los Angeles EMAIL Christina Fragouli University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 Communication efficient for contextual linear bandits with known distribution (...) Algorithm 2 Communication efficient for contextual linear bandits with unknown distribution |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]. The paper does not provide any links or explicit statements about open-source code for the described methodology. |
| Open Datasets | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]. The paper is theoretical and describes contexts being "generated from a distribution" but does not specify or provide access information for any publicly available or open dataset used for training. |
| Dataset Splits | No | The paper explicitly states "N/A" for running experiments in its checklist and does not discuss any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper explicitly states "N/A" for running experiments in its checklist and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper explicitly states "N/A" for running experiments in its checklist and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper explicitly states "N/A" for running experiments in its checklist and does not describe any experimental setup details such as hyperparameters or training configurations. |