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 [1].
ActiveHedge: Hedge meets Active Learning
Authors: Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide preliminary experiments to compare Active Hedge (Algorithm 2), with standard Hedge (Algorithm 1) and the label efficient algorithm given by Cesa-Bianchi et al. (2005). |
| Researcher Affiliation | Academia | 1Georgia Institute of Technology 2Department of Electrical and Computer Engineering, Boston University. |
| Pseudocode | Yes | Algorithm 1: Hedge |
| Open Source Code | No | The paper does not provide explicit statements or links for open-source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data for experiments ('We uniformly N sample linear classifiers from a unit sphere centred at origin. We then sample M points from a unit sphere and classify each point using the N experts to create the expert prediction matrix X.') rather than using a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | All experiments are repeated 100 times, with M = 10000 and N = 100 and d = 10. We use upper bounds for ζ and ϵ and other parameters are set optimally. |