Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition
Authors: Yining Wang, Aarti Singh
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present a simple noise-robust margin-based active learning algorithm to find homogeneous (passing the origin) linear separators and analyze its error convergence when labels are corrupted by noise. We show that when the imposed noise satisfies the Tsybakov low noise condition...the algorithm is able to adapt to unknown level of noise and achieves optimal statistical rate up to polylogarithmic factors. We also derive lower bounds for margin based active learning algorithms under Tsybakov noise conditions (TNC)...Our proof involves the construction of a wellseparated hypothesis set on the d-dimensional unit ball along with carefully designed label distributions for the Tsybakov noise condition. |
| Researcher Affiliation | Academia | Yining Wang and Aarti Singh Machine Learning Department, School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh PA 15213 |
| Pseudocode | Yes | Algorithm 1 A noise-adaptive margin-based active learning algorithm |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and refers to a 'uniform distribution on the unit ball Sd' as a mathematical concept, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical validation using dataset splits like training, validation, or testing sets. Algorithm 1 includes parameters related to its theoretical operation, not empirical data splitting. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes an algorithm with theoretical parameters (e.g., 'data dimension d, query budget T, failure probability δ, shrinkage rate r') rather than concrete hyperparameters or system-level training settings for an empirical experiment. |