Fast Co-Training under Weak Dependence via Stream-Based Active Learning
Authors: Ilias Diakonikolas, Mingchen Ma, Lisheng Ren, Christos Tzamos
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <mingchen@cs.wisc.edu>, Lisheng Ren <lren29@wisc.edu>. |
| 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. |