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..
Bandit Online Linear Optimization with Hints and Queries
Authors: Aditya Bhaskara, Ashok Cutkosky, Ravi Kumar, Manish Purohit
ICML 2023 | Venue PDF | 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. |