Add to ORKGWe propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general dis-tributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning settings (such as classification), in regression the passive learning rate of O(1/) cannot in general be improved upon. Nonetheless, the so-called ‘constant ’ in the rate of convergence, which is characterized by a distribution-dependent risk, can be improved in many cases. For a given distribution, achieving the optimal risk re-quires prior knowledge of the distribution. Following the stratification technique advocated in Mont...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
In many real world supervised learning problems, it is easy or cheap to acquire unlabelled data, but...
Abstract We address problems of model misspeci\u85cation in active learning. We suppose that an inve...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
International audienceWe study active learning as a derandomized form of sampling. We show that full...
This thesis presents a general discussion of active learning and adaptive sampling. In many practica...
<p>This thesis makes fundamental computational and statistical advances in testing and estimation, m...
This paper analyzes the potential advantages and theoretical challenges of "active learning" algorit...
Optimal active learning refers to a framework where the learner actively selects data points to be a...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
For many supervised learning tasks, the cost of acquiring training data is dominated by the cost of ...
We consider the problem of actively learning the mean values of distributions associated with a fini...
We consider the problem of online active learning to collect data for regression modeling. Specifica...
Active learning is a type of sequential design for supervised machine learning, in which the learnin...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
In many real world supervised learning problems, it is easy or cheap to acquire unlabelled data, but...
Abstract We address problems of model misspeci\u85cation in active learning. We suppose that an inve...
This paper presents a rigorous statistical analysis characterizing regimes in which active learning ...
International audienceWe study active learning as a derandomized form of sampling. We show that full...
This thesis presents a general discussion of active learning and adaptive sampling. In many practica...
<p>This thesis makes fundamental computational and statistical advances in testing and estimation, m...
This paper analyzes the potential advantages and theoretical challenges of "active learning" algorit...
Optimal active learning refers to a framework where the learner actively selects data points to be a...
In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant...
For many supervised learning tasks, the cost of acquiring training data is dominated by the cost of ...
We consider the problem of actively learning the mean values of distributions associated with a fini...
We consider the problem of online active learning to collect data for regression modeling. Specifica...
Active learning is a type of sequential design for supervised machine learning, in which the learnin...
AbstractWe state and analyze the first active learning algorithm that finds an ϵ-optimal hypothesis ...
Over the last decade there has been growing interest in pool-based active learning techniques, where...
In many real world supervised learning problems, it is easy or cheap to acquire unlabelled data, but...