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..
Scaling Active Search using Linear Similarity Functions
Authors: Sibi Venkatesan, James K. Miller, Jeff Schneider, Artur Dubrawski
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that our method is competitive with existing semi-supervised approaches. |
| Researcher Affiliation | Academia | Sibi Venkatesan, James K. Miller, Jeff Schneider and Artur Dubrawski Auton Lab, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 LAS: Linearized Active Search |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | We performed experiments on the following datasets: the Cover Type and Adult datasets from the UCI Machine Learning Repository and MNIST. |
| Dataset Splits | No | The paper does not provide specific train/validation/test splits. It describes an active learning setup where points are iteratively queried and moved from an unlabeled set to a labeled set, and initializes with one randomly chosen positive. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For LAS, we took α (the coefficient for the Impact Factor) to be the best from empirical evaluations. This was 10 6 for Cover Type and Adult, and 0 for MNIST. π was taken as the true positives prevalence. |