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..
Training-Time Optimization of a Budgeted Booster
Authors: Yi Huang, Brian Powers, Lev Reyzin
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that our method improves upon the boosting approach Ada Boost RS [Reyzin, 2011] and in many cases also outperforms the recent algorithm Speed Boost [Grubb and Bagnell, 2012]. We provide a theoretical justication for our optimization method via the margin bound. We also experimentally show that our method outperforms pruned decision trees, a natural budgeted classifier. and 6 Experimental results Although there are a number of feature-efficient classification methods [Gao and Koller, 2011; Schwing et al., 2011; Xu et al., 2012], we directly compare the performance of Ada Boost BT, Ada Boost BT Greedy and Ada Boost BT Smoothed to Ada Boost RS and Speed Boost as both are feature-efficient boosting methods which allow for any class of weak learners. For our experiments we first used datasets from the UCI repository, as shown in Table 1. |
| Researcher Affiliation | Academia | Yi Huang, Brian Powers, Lev Reyzin Department of Mathematics, Statistics, and Computer Science University of Illinois at Chicago Chicago, IL 60607 EMAIL |
| Pseudocode | Yes | Algorithm 1 Ada Boost BT(S,B,C), where: S X { 1, +1}, B > 0, C : [i . . . n] R+ |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | For our experiments we first used datasets from the UCI repository, as shown in Table 1. ... Then, to study our algorithms on real-world data, we used the Yahoo! Webscope dataset 2, which includes feature costs [Xu et al., 2012]. |
| Dataset Splits | No | Table 1 provides 'training size' and 'test size' for the datasets, and figures mention 'test error', but there is no explicit mention of a 'validation' split or its size/methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory specifications). |
| Software Dependencies | No | The paper mentions algorithms and components like 'Ada Boost' and 'decision stumps' and 'exponential loss', but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | For our experiments we first used datasets from the UCI repository, as shown in Table 1. ... Ada Boost was run for a number of rounds that gave lowest test error, irrespective of budget. This setup was chosen to compare directly against the results of Reyzin [2011] who also used random costs. ... Features are given costs uniformly at random on the interval [0, 2]. ... The data set contains 519 features, whose costs we rescaled to costs to the set {.1, .5, 1, 2, 5, 10, 15, 20}. |