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..
Fast Co-Training under Weak Dependence via Stream-Based Active Learning
Authors: Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work presents theoretical results on certain topics of co-training and active learning. The goal is to advance the field of Machine Learning. |
| Researcher Affiliation | Academia | 1Department of Computer Sciences, University of Wisconsin-Madison, Madison, USA 2University of Athens and Archimedes AI, Athens, Greece. Correspondence to: Mingchen Ma <EMAIL>, Lisheng Ren <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 REDUCTION(A1, A2) (Efficient Black-Box Reduction from Co-Training to Online Learning)... Algorithm 2 LEARNK-INTERVAL (Efficient co-training k intervals)... Algorithm 3 CO-HALVING(H) (Co-training VC classes via Halving)... Algorithm 4 LEARNK-INTERVAL (Efficient co-training k intervals)... Algorithm 5 Co-training Halfspaces without Margin with Label Queries... Algorithm 6 Subroutine for Co-training Partial Classifier using Label Queries |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe experiments performed on publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |