A Drifting-Games Analysis for Online Learning and Applications to Boosting
Authors: Haipeng Luo, Robert E. Schapire
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we translate our new Hedge algorithm into a new adaptive boosting algorithm that is computationally faster as shown in experiments, since it ignores a large number of examples on each round. |
| Researcher Affiliation | Collaboration | Haipeng Luo Department of Computer Science Princeton University Princeton, NJ 08540 haipengl@cs.princeton.edu Robert E. Schapire Department of Computer Science Princeton University Princeton, NJ 08540 schapire@cs.princeton.edu R. Schapire is currently at Microsoft Research in New York City. |
| Pseudocode | Yes | Input: A Hedge Algorithm H for t = 1 to T do Query H: pt = H( 1:t 1). Set: DR(z1:t 1) = pt. Receive movements zt from the adversary. Set: t,i = zt,i minj zt,j, 8i. Algorithm 1: Conversion of a Hedge Algorithm H to a DGv1 Algorithm DR |
| Open Source Code | No | The paper does not contain any explicit statement about releasing its source code or provide a link to a code repository for the methodology described. |
| Open Datasets | No | The paper mentions experiments using 'Real.All' but does not provide concrete access information (link, DOI, repository name, formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper discusses training and test error but does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and test sets. |
| Hardware Specification | No | The paper conducts experiments but does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, used to replicate the experiments. |
| Experiment Setup | No | The paper mentions running experiments and compares algorithms like Ada Boost, NH-Boost, and NH-Boost.DT, but it does not provide specific details such as hyperparameter values, optimization settings, or other system-level training configurations needed to reproduce the experimental setup. |