Online Learning with Abstention
Authors: Corinna Cortes, Giulia DeSalvo, Claudio Gentile, Mehryar Mohri, Scott Yang
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further report the results of a series of experiments demonstrating that UCB-GT largely outperforms that extension of UCB-N, as well as other standard baselines. |
| Researcher Affiliation | Collaboration | 1Google Research, New York, NY. 2INRIA Lille Nord Europe. 3Courant Institute of Mathematical Sciences, New York, NY. 4D. E. Shaw & Co., New York, NY. |
| Pseudocode | Yes | ALGORITHM 1: UCB-NT... ALGORITHM 2: UCB-GT |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described. |
| Open Datasets | Yes | We used the following eight datasets from the UCI data repository: HIGGS, phishing, ijcnn, covtype, eye, skin, cod-rna, and guide. We also used the CIFAR dataset from (Krizhevsky et al., 2009) |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test dataset splits. It mentions 'averaged over five random draws of the data'. |
| 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 software dependencies with version numbers. |
| Experiment Setup | Yes | The predictors h are hyperplanes centered at the origin whose normal vector in Rd is drawn randomly from the Gaussian distribution, N(0, 1)d, where d is the dimension of the feature space of the dataset. The abstention functions r are concentric annuli around the origin with radii in (0, d 20 . . . , d). For each dataset, we generated a total of K = 2,100 experts and all the algorithms were tested for a total of T = 10,000 rounds. We report these results for c 2 {0.1, 0.2, 0.3}. |