Search Results for author:
Found 3 papers, 0 papers with code
Online Active Learning with Surrogate Loss Functions
We derive a novel active learning algorithm in the streaming setting for binary classification tasks.
Nonstochastic Multiarmed Bandits with Unrestricted Delays
We then introduce a new algorithm that lifts the requirement of bounded delays by using a wrapper that skips rounds with excessively large delays.
Adaptation to Easy Data in Prediction with Limited Advice
In addition, we show that in the stochastic setting SODA achieves an $O\left(\sum_{a:\Delta_a>0} \frac{K^3 \varepsilon^2}{\Delta_a}\right)$ pseudo-regret bound that holds simultaneously with the adversarial regret guarantee.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
