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Online learning: A comprehensive survey
Online learning represents a family of machine learning methods, where a learner attempts
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
to tackle some predictive (or any type of decision-making) task by learning from a sequence …
A survey on distributed online optimization and online games
Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …
A modern introduction to online learning
In this monograph, I introduce the basic concepts of Online Learning through a modern view
of Online Convex Optimization. Here, online learning refers to the framework of regret …
of Online Convex Optimization. Here, online learning refers to the framework of regret …
Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Online meta-learning
A central capability of intelligent systems is the ability to continuously build upon previous
experiences to speed up and enhance learning of new tasks. Two distinct research …
experiences to speed up and enhance learning of new tasks. Two distinct research …
On the optimization of deep networks: Implicit acceleration by overparameterization
Conventional wisdom in deep learning states that increasing depth improves
expressiveness but complicates optimization. This paper suggests that, sometimes …
expressiveness but complicates optimization. This paper suggests that, sometimes …
Adaptive gradient-based meta-learning methods
We build a theoretical framework for designing and understanding practical meta-learning
methods that integrates sophisticated formalizations of task-similarity with the extensive …
methods that integrates sophisticated formalizations of task-similarity with the extensive …
Self-paced ARIMA for robust time series prediction
For time series prediction tasks, the autoregressive integrated moving average (ARIMA)
model is one of the most classical and popular linear models, and extended applications …
model is one of the most classical and popular linear models, and extended applications …
Introduction to online convex optimization
This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …
[ספר][B] Machine learning: a Bayesian and optimization perspective
S Theodoridis - 2015 - books.google.com
This tutorial text gives a unifying perspective on machine learning by covering both
probabilistic and deterministic approaches-which are based on optimization techniques …
probabilistic and deterministic approaches-which are based on optimization techniques …