Browsing by Author "van der Schaar, M."

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Open Access
Actionable intelligence and online learning for semantic computing
(World Scientific Publishing Company, 2017) Tekin, Cem; van der Schaar, M.
As the world becomes more connected and instrumented, high dimensional, heterogeneous and time-varying data streams are collected and need to be analyzed on the fly to extract the actionable intelligence from the data streams and make timely decisions based on this knowledge. This requires that appropriate classifiers are invoked to process the incoming streams and find the relevant knowledge. Thus, a key challenge becomes choosing online, at run-time, which classifier should be deployed to make the best possible predictions on the incoming streams. In this paper, we survey a class of methods capable to perform online learning in stream-based semantic computing tasks: multi-armed bandits (MABs). Adopting MABs for stream mining poses, numerous new challenges requires many new innovations. Most importantly, the MABs will need to explicitly consider and track online the time-varying characteristics of the data streams and to learn fast what is the relevant information out of the vast, heterogeneous and possibly highly dimensional data streams. In this paper, we discuss contextual MAB methods, which use similarities in context (meta-data) information to make decisions, and discuss their advantages when applied to stream mining for semantic computing. These methods can be adapted to discover in real-time the relevant contexts guiding the stream mining decisions, and tract the best classifier in presence of concept drift. Moreover, we also discuss how stream mining of multiple data sources can be performed by deploying cooperative MAB solutions and ensemble learning. We conclude the paper by discussing the numerous other advantages of MABs that will benefit semantic computing applications.
Open Access
Online learning in limit order book trade execution
(Institute of Electrical and Electronics Engineers, 2018) Akbarzadeh, N.; Tekin, Cem; van der Schaar, M.
In this paper, we propose an online learning algorithm for optimal execution in the limit order book of a financial asset. Given a certain number of shares to sell and an allocated time window to complete the transaction, the proposed algorithm dynamically learns the optimal number of shares to sell via market orders at prespecified time slots within the allocated time interval. We model this problem as a Markov Decision Process (MDP), which is then solved by dynamic programming. First, we prove that the optimal policy has a specific form, which requires either selling no shares or the maximum allowed amount of shares at each time slot. Then, we consider the learning problem, in which the state transition probabilities are unknown and need to be learned on the fly. We propose a learning algorithm that exploits the form of the optimal policy when choosing the amount to trade. Interestingly, this algorithm achieves bounded regret with respect to the optimal policy computed based on the complete knowledge of the market dynamics. Our numerical results on several finance datasets show that the proposed algorithm performs significantly better than the traditional Q-learning algorithm by exploiting the structure of the problem.