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A New Approach to Drifting Games, Based on Asymptotically Optimal Potentials
We develop a new approach to drifting games, a class of two-person games with many applications to boosting and online learning settings.
Fast Rates in Pool-Based Batch Active Learning
We consider a batch active learning scenario where the learner adaptively issues batches of points to a labeling oracle.
Neural Active Learning with Performance Guarantees
We investigate the problem of active learning in the streaming setting in non-parametric regimes, where the labels are stochastically generated from a class of functions on which we make no assumptions whatsoever.
New Potential-Based Bounds for the Geometric-Stopping Version of Prediction with Expert Advice
To obtain explicit bounds, we construct potentials for the geometric version from potentials used for the fixed horizon version of the problem.
New Potential-Based Bounds for Prediction with Expert Advice
This work addresses the classic machine learning problem of online prediction with expert advice.
