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We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the f...
Geosciences deal with complex, interdependent landscape processes, of which hydrological processes are an important example. Understanding such processes in time and space is crucial in geoscientific ...
In this paper, we investigate the power of online learning in stochastic network optimization with unknown system statistics a priori. We are interested in understanding how information and learning c...
Background: Online courses in healthcare programs like Dietetics have increased in availability and popularity. Objective: The purpose of this study was to investigate the connections between online l...
Objective. To assess factors influencing student engagement with e-tools used as a learning supplement to the standard curriculum in pharmacology courses. Design. A suite of 148 e-tools (interactive ...
Normalized Online Learning     Normalized  Online Learning       2013/6/17
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has se...
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitab...
In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample...
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed...
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...
This paper examines trends, potential and challenges in online learning. We consider 6 online learning trends and the issues associated with the trends. These include a one sided emphasis on the techn...
We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Speci cally if the sequence encountered by the learner is described well by a know...
There are three serious problems facing education today. These include working with draconian budgets that mean reduced spending for education, making education interesting and relevant for students, ...
Improvements in hybrid electric vehicle (HEV) fuel economy and emissions heavily depend on an efficient energy management strategy (EMS). However, the uncertainty of future driving conditions generall...
Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader.

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