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..
Linear Contextual Bandits with Knapsacks
Authors: Shipra Agrawal, Nikhil Devanur
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present algorithms with near-optimal regret bounds for this problem. Our bounds compare favorably to results on the unstructured version of the problem [5, 10] where the relation between the contexts and the outcomes could be arbitrary, but the algorithm only competes against a fixed set of policies accessible through an optimization oracle. We combine techniques from the work on lin Contextual, Bw K and OSPP in a nontrivial manner while also tackling new difficulties that are not present in any of these special cases. |
| Researcher Affiliation | Collaboration | Columbia University. EMAIL. Microsoft Research. EMAIL. |
| Pseudocode | Yes | Algorithm 1 Algorithm for lin CBw K, with given Z ... Algorithm 2 Algorithm for lin CBw K, with Z computation |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret bounds. It does not describe experiments using specific datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, including hyperparameters or training configurations. |