The Power of Comparisons for Actively Learning Linear Classifiers
Authors: Max Hopkins, Daniel Kane, Shachar Lovett
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To confirm our theoretical findings, we have implemented a variant of Theorem 3.7 for finite samples. For a given sample size or dimension, the query complexity we present is averaged over 500 trials of the algorithm. |
| Researcher Affiliation | Academia | Max Hopkins Dept. of Computer Science and Engineering University of California, San Diego La Jolla, CA 92092 nmhopkin@eng.ucsd.edu Daniel Kane Dept. of Computer Science and Engineering University of California, San Diego La Jolla, CA 92092 dakane@eng.ucsd.edu Shachar Lovett Dept. of Computer Science and Engineering University of California, San Diego La Jolla, CA 92092 slovett@cs.ucsd.edu |
| Pseudocode | Yes | Algorithm 1: Perfect-Learning(N, Q, d, c) |
| Open Source Code | No | The paper states it implemented a variant of Theorem 3.7, but does not provide any concrete access to source code for its methodology, nor does it explicitly state that the code is being released. |
| Open Datasets | No | The paper states "our algorithm labels finite samples drawn from the uniform distribution over Bd." However, it does not provide concrete access information (link, DOI, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For a given sample size or dimension, the query complexity we present is averaged over 500 trials of the algorithm. We first note a few practical modifications. First, our algorithm labels finite samples drawn from the uniform distribution over Bd. Second, to match our methodology in lower bounding Label-Pool-RPU learning, we will draw our classifier uniformly from hyperplanes tangent to the unit ball. Finally, because the true inference dimension of the sample might be small, our algorithm guesses a low potential inference dimension to start, and doubles its guess on each iteration with low coverage. |