Bandit Online Linear Optimization with Hints and Queries
Authors: Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we include an experimental evaluation of Algorithm 1 on synthetic data. |
| Researcher Affiliation | Collaboration | 1University of Utah, Salt Lake City, UT, USA 2Boston University, Boston, MA, USA 3Google Research, Mountain View, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Bandit OLO with Queries. Algorithm 2 Bandit OLO with Response Feedback. Algorithm 3 Hint Weight Learner |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available, nor does it include links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper states that data is generated synthetically for experiments: "For each time step t independently, the cost vector ct is generated as follows: the first coordinate of ct is fixed to be 0.5, and the remaining d 1 coordinates are drawn uniformly at random from a (d 1)-dimensional sphere of radius 1 0.52 so that each cost vector has unit length." and "ct = p (1, 0, 0) + (1 p) ut where ut is a uniformly random unit vector on the sphere in R3." |
| Dataset Splits | No | The paper describes synthetic data generation for online learning experiments, but it does not specify explicit training/validation/test dataset splits. |
| Hardware Specification | No | The paper describes the experimental setup and data generation but does not provide any specific details regarding the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper describes the experimental setup but does not specify any software dependencies or their version numbers (e.g., specific libraries, frameworks, or solvers with version information). |
| Experiment Setup | Yes | For each time step t independently, the cost vector ct is generated as follows: the first coordinate of ct is fixed to be 0.5, and the remaining d 1 coordinates are drawn uniformly at random from a (d 1)-dimensional sphere of radius 1 0.52 so that each cost vector has unit length. We set B = 0, i.e., there are no bad query responses and set the time horizon T = 5000. |