8 citations · 12 across the 3 of their papers we have counts for
5 papers
Dynamic Regret Reduces to Kernelized Static Regret
Andrew Jacobsen, Alessandro Rudi, Francesco Orabona +1
We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competin…
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data
Rocco De Rosa
Decision tree classifiers are a widely used tool in data stream mining. The use of confidence intervals to estimate the gain associated with each split leads to very effective meth…
Active Learning for Online Recognition of Human Activities from Streaming Videos
Rocco De Rosa, Ilaria Gori, Fabio Cuzzolin +2
Recognising human activities from streaming videos poses unique challenges to learning algorithms: predictive models need to be scalable, incrementally trainable, and must remain b…
The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams
Rocco De Rosa, Francesco Orabona, Nicolò Cesa-Bianchi
Stream mining poses unique challenges to machine learning: predictive models are required to be scalable, incrementally trainable, must remain bounded in size (even when the data s…
A Generalized Online Mirror Descent with Applications to Classification and Regression
Francesco Orabona, Koby Crammer, Nicolò Cesa-Bianchi
Online learning algorithms are fast, memory-efficient, easy to implement, and applicable to many prediction problems, including classification, regression, and ranking. Several onl…