207 citations · 435 across the 12 of their papers we have counts for
4 papers · 1 filter
Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin, Andreas Krause, Debajyoti Ray
We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypoth…
The B-Skip-List: A Simpler Uniquely Represented Alternative to B-Trees
Daniel Golovin
In previous work, the author introduced the B-treap, a uniquely represented B-tree analogue, and proved strong performance guarantees for it. However, the B-treap maintains complex…
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization
Daniel Golovin, Andreas Krause
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficu…
Online Distributed Sensor Selection
Daniel Golovin, Matthew Faulkner, Andreas Krause
A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to con…