Online Active Learning with Surrogate Loss Functions
We derive a novel active learning algorithm in the streaming setting for binary classification tasks. The algorithm leverages weak labels to minimize the number of label requests, and trains a model to optimize a surrogate loss on a resulting set of labeled and weak-labeled points. Our algorithm jointly admits two crucial properties: theoretical guarantees in the general agnostic setting and a strong empirical performance. Our theoretical analysis shows that the algorithm attains favorable generalization and label complexity bounds, while our empirical study on 18 real-world datasets demonstrate that the algorithm outperforms standard baselines, including the Margin Algorithm, or Uncertainty Sampling, a high-performing active learning algorithm favored by practitioners.
Code (0)
등록된 구현이 없습니다.
Tasks
Active LearningBinary ClassificationSimilar Papers 제목 키워드 기반
Surrogate Losses in Passive and Active Learning
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large pool of unlabeled data points. The objec…
Active LearningLocality-aware Surrogates for Gradient-based Black-box Optimization
In physics and engineering, many processes are modeled using non-differentiable black-box simulators, making the optimization of such functions particularly challenging. To address such cases, inspired by the Gradient Th…
Computational EfficiencyRONAALP: Reduced-Order Nonlinear Approximation with Active Learning Procedure
Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Active Learning Proced…
Active LearningUniversal Online Convex Optimization with $1$ Projection per Round
To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain minimax rates for multiple types of convex …
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models
Existing active strategies for training surrogate models yield accurate structural reliability estimates by aiming at design space regions in the vicinity of a specified limit state function. In many practical engineerin…
Active Learning