488,035 research outputs found
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Despite the recent success of representation learning in sequential decision
making, the study of the pure exploration scenario (i.e., identify the best
option and minimize the sample complexity) is still limited. In this paper, we
study multi-task representation learning for best arm identification in linear
bandits (RepBAI-LB) and best policy identification in contextual linear bandits
(RepBPI-CLB), two popular pure exploration settings with wide applications,
e.g., clinical trials and web content optimization. In these two problems, all
tasks share a common low-dimensional linear representation, and our goal is to
leverage this feature to accelerate the best arm (policy) identification
process for all tasks. For these problems, we design computationally and sample
efficient algorithms DouExpDes and C-DouExpDes, which perform double
experimental designs to plan optimal sample allocations for learning the global
representation. We show that by learning the common representation among tasks,
our sample complexity is significantly better than that of the native approach
which solves tasks independently. To the best of our knowledge, this is the
first work to demonstrate the benefits of representation learning for
multi-task pure exploration
Evaluation of e-learning web sites using fuzzy axiomatic design based approach
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations for future research are offered
Accessible user interface support for multi-device ubiquitous applications: architectural modifiability considerations
The market for personal computing devices is rapidly expanding from PC, to mobile, home entertainment systems, and even the automotive industry. When developing software targeting such ubiquitous devices, the balance between development costs and market coverage has turned out to be a challenging issue. With the rise of Web technology and the Internet of things, ubiquitous applications have become a reality. Nonetheless, the diversity of presentation and interaction modalities still drastically limit the number of targetable devices and the accessibility toward end users. This paper presents webinos, a multi-device application middleware platform founded on the Future Internet infrastructure. Hereto, the platform's architectural modifiability considerations are described and evaluated as a generic enabler for supporting applications, which are executed in ubiquitous computing environments
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