274 citations · 480 across the 29 of their papers we have counts for
4 papers · 1 filter
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds
Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy +2
We design a new algorithm for batch active learning with deep neural network models. Our algorithm, Batch Active learning by Diverse Gradient Embeddings (BADGE), samples groups of…
Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case
Alina Beygelzimer, Dávid Pál, Balázs Szörényi +3
We study the problem of efficient online multiclass linear classification with bandit feedback, where all examples belong to one of classes and lie in the -dimensional Eucli…
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins +1
We study contextual bandit learning with an abstract policy class and continuous action space. We obtain two qualitatively different regret bounds: one competes with a smoothed ver…
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé +2
We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data. We specifically consider settings in which the underlying le…