We develop a new active learning algorithm for the streaming setting
satisfying three important properties: 1) It provably works for any classifier
representation and classification problem including those with severe noise. 2)
It is efficiently implementable with an ERM oracle. 3) It is more aggressive
than all previous approaches satisfying 1 and 2. To do this we create an
algorithm based on a newly defined optimization problem and analyze it. We also
conduct the first experimental analysis of all efficient agnostic active
learning algorithms, evaluating their strengths and weaknesses in different
settings.

本文提出一种基于新的优化问题的主动学习算法，在流式环境下，可有效地解决任何分类器表示和分类问题，且具有高效实现的特点，并证明了该算法比现有的所有满足前两个特点的算法更为优秀，并对所有高效的主动学习算法进行了第一次实验分析，评估了它们在不同场景下的优劣。

高效简约迁移主动学习

Efficient and Parsimonious Agnostic Active Learning

A fundamental problem in control is to learn a model of a system from
observations that is useful for controller synthesis. To provide good
performance guarantees, existing methods must assume that the real system is in
the class of models considered during learning. We present an iterative method
with strong guarantees even in the agnostic case where the system is not in the
class. In particular, we show that any no-regret online learning algorithm can
be used to obtain a near-optimal policy, provided some model achieves low
training error and access to a good exploration distribution. Our approach
applies to both discrete and continuous domains. We demonstrate its efficacy
and scalability on a challenging helicopter domain from the literature.

通过在线学习算法获取训练误差较小的模型，并使用良好的探索分布可以获得近乎最优的策略，适用于离散和连续域，并具有强大保证性。

基于模型的强化学习中的不可知系统识别

Agnostic System Identification for Model-Based Reinforcement Learning

We present and analyze an agnostic active learning algorithm that works
without keeping a version space. This is unlike all previous approaches where a
restricted set of candidate hypotheses is maintained throughout learning, and
only hypotheses from this set are ever returned. By avoiding this version space
approach, our algorithm sheds the computational burden and brittleness
associated with maintaining version spaces, yet still allows for substantial
improvements over supervised learning for classification.

本文介绍了一种不需要保留版本空间，具有不确定性主动学习算法，并且相较于监督学习和分类，能够显着提高学习效果。