Feedback Coding for Active Learning

by   Gregory Canal, et al.

The iterative selection of examples for labeling in active machine learning is conceptually similar to feedback channel coding in information theory: in both tasks, the objective is to seek a minimal sequence of actions to encode information in the presence of noise. While this high-level overlap has been previously noted, there remain open questions on how to best formulate active learning as a communications system to leverage existing analysis and algorithms in feedback coding. In this work, we formally identify and leverage the structural commonalities between the two problems, including the characterization of encoder and noisy channel components, to design a new algorithm. Specifically, we develop an optimal transport-based feedback coding scheme called Approximate Posterior Matching (APM) for the task of active example selection and explore its application to Bayesian logistic regression, a popular model in active learning. We evaluate APM on a variety of datasets and demonstrate learning performance comparable to existing active learning methods, at a reduced computational cost. These results demonstrate the potential of directly deploying concepts from feedback channel coding to design efficient active learning strategies.


Evaluation of Seed Set Selection Approaches and Active Learning Strategies in Predictive Coding

Active learning is a popular methodology in text classification - known ...

Improved active output selection strategy for noisy environments

The test bench time needed for model-based calibration can be reduced wi...

Sample Noise Impact on Active Learning

This work explores the effect of noisy sample selection in active learni...

Automatic Playtesting for Game Parameter Tuning via Active Learning

Game designers use human playtesting to gather feedback about game desig...

Evaluating Sentence-Level Relevance Feedback for High-Recall Information Retrieval

This study uses a novel simulation framework to evaluate whether the tim...

Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset

Active Learning (AL) has remained relatively unexplored for LiDAR percep...

Please sign up or login with your details

Forgot password? Click here to reset