Sample-Efficient Agnostic Boosting
Authors: Udaya Ghai, Karan Singh
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In preliminary experiments in Section 7, we demonstrate that the sample complexity improvements of our approach do indeed manifest in the form of improved empirical performance over previous agnostic boosting approaches on commonly used datasets. 7 Experiments In Table 3, we report the results of preliminary experiments with Algorithm 1 against the agnostic boosting algorithms in [KK09] and [BCHM20] as baselines on UCI classification datasets [SWHB89, HRFS99, SED+88], using decision stumps [PVG+11] as weak learners. |
| Researcher Affiliation | Collaboration | Udaya Ghai Amazon ughai@amazon.com Karan Singh Tepper School of Business Carnegie Mellon University karansingh@cmu.edu |
| Pseudocode | Yes | Algorithm 1 Agnostic Boosting via Sample Reuse |
| Open Source Code | No | The paper describes implementation details in Appendix J, but does not provide a direct link to open-source code or an explicit statement of code availability for the described methodology. |
| Open Datasets | Yes | In Table 3, we report the results of preliminary experiments with Algorithm 1 against the agnostic boosting algorithms in [KK09] and [BCHM20] as baselines on UCI classification datasets [SWHB89, HRFS99, SED+88], using decision stumps [PVG+11] as weak learners. |
| Dataset Splits | Yes | Accuracy is estimated using 30-fold cross validation with a grid search over the mixing weight σ and the number of boosters T. |
| Hardware Specification | Yes | The runtime used for all experiments was a Google Cloud Engine VM instance with 2 v CPUs (Intel Xeon 64-bit @ 2.20 GHz) and 12 GB RAM. |
| Software Dependencies | No | The paper mentions using "decision stumps [PVG+11]" and referencing "Scikit-learn: Machine learning in Python" in the citation, but does not specify version numbers for any software dependencies. |
| Experiment Setup | Yes | For all algorithms, we perform a grid search on the number of boosting rounds with T {25, 50, 100}. For Algorithm 1 we search over σ {0.1, 0.25, 0.5} as well. Rather than using a fixed η, our implementation uses an adaptive step-size scheme proportional to the empirical correlation on the current relabeled distribution. |