Linear Contextual Bandits with Knapsacks
Authors: Shipra Agrawal, Nikhil Devanur
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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. sa3305@columbia.edu. Microsoft Research. nikdev@microsoft.com. |
| 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. |