4 accepted papers
We consider interactive learning in the realizable setting and develop a general framework to handle problems ranging from best arm identification to active classification. We begin our investigation with the observation that agnostic algorithms \emph{cannot} be minimax-optimal in the realizable set…
In this paper we provide simple, computationally efficient, active algorithms for completion of symmetric positive semidefinite matrices. Our proposed algorithms are based on adaptive Nyström sampling, and are allowed to actively query any element in the matrix, and obtain a possibly noisy estimate…
The goal of ordinal embedding is to represent items as points in a low-dimensional Euclidean space given a set of constraints like ``item $i$ is closer to item $j$ than item $k$''. Ordinal constraints like this often come from human judgments. The classic approach to solving this problem is know…
Active learning methods automatically adapt data collection by selecting the most informative samples in order to accelerate machine learning. Because of this, real-world testing and comparing active learning algorithms requires collecting new datasets (adaptively), rather than simply applying algor…