핵심 개념
Utilizing Wasserstein distance and GroupSort Neural Networks in active learning for regression improves estimation accuracy and convergence speed.
초록
The content discusses a novel active learning strategy for regression problems using Wasserstein distance and GroupSort Neural Networks. It addresses the challenges of data collection, labeling, and estimation accuracy in machine learning. The method combines uncertainty-based approaches with representativity to select relevant data points efficiently. The use of GroupSort neural networks provides theoretical foundations for accurate estimation and faster convergence compared to traditional methods.
Abstract:
- Introduces a new active learning strategy for regression problems.
- Utilizes Wasserstein distance and GroupSort Neural Networks.
- Focuses on distribution-matching principles to measure dataset representativeness.
Introduction:
- Challenges in data collection and labeling in machine learning.
- Discusses the need for efficient estimation with limited labeled data.
- Mentions few-shot learning, transfer learning, generative adversarial models as alternative solutions.
Active Learning Framework:
- Describes the framework for performing active learning in regression tasks.
- Defines unknown functions, sample subsets, estimators, and loss functions.
- Outlines the empirical counterpart of expected error risk.
Wasserstein Distance:
- Defines the Wasserstein distance between probability measures.
- Discusses Kantorovich-Rubinstein duality and Lipschitz functions.
- Highlights the importance of 1-Lipschitz functions in estimating Wasserstein distance accurately.
Group Sort Neural Networks:
- Introduces Group Sort activation function and neural network architecture.
- Discusses assumptions about matrix norms, Lipschitz functions, and network depth/size requirements.
Numerical Experiments:
- Compares WAR model with other query strategies on UCI datasets.
- Presents RMSE values after querying 25% of the dataset.
통계
The presented Wasserstein active regression model achieves more precise estimations faster than other models.
인용구
"The study empirically shows the pertinence of such a representativity–uncertainty approach."
"The use of such networks provides theoretical foundations giving a way to quantify errors with explicit bounds."