We study a scenario of active learning where the input space is partitioned into different
regions and where a distinct hypothesis is learned for each region. We first introduce a new …

Adaptivity to Noise Parameters in Nonparametric Active Learning Page 1 Proceedings of
Machine Learning Research vol 65:1–34, 2017 Adaptivity to Noise Parameters in …

To date, the tightest upper and lower-bounds for the active learning of general concept
classes have been in terms of a parameter of the learning problem called the splitting index …

We propose a pool-based non-parametric active learning algorithm for general metric
spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which …

Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label
at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively …

We present a new active learning algorithm that adaptively partitions the input space into a
finite number of regions, and subsequently seeks a distinct predictor for each region, while …

We generalize the notion of average Lipschitz smoothness proposed by Ashlagi et al.(COLT
2021) by extending it to Hölder smoothness. This measure of the" effective smoothness" of a …

Online machine learning systems need to adapt to domain shifts. Meanwhile, acquiring label
at every timestep is expensive. We propose a surprisingly simple algorithm that adaptively …

We study distribution-free nonparametric regression following a notion of average
smoothness initiated by Ashlagi et al.(2021), which measures the “effective” smoothness of a …

We investigate active learning by pairwise similarity over the leaves of trees originating from
hierarchical clustering procedures. In the realizable setting, we provide a full …