Stochastic Graph Bandit Learning with Side-Observations

In this paper, we investigate the stochastic contextual bandit with general function space and graph feedback. We propose an algorithm that addresses this problem by adapting to both the underlying graph structures and reward gaps. To the best of our knowledge, our algorithm is the first to provide a gap-dependent upper bound in this stochastic setting, bridging the research gap left by the work in [35]. In comparison to [31,33,35], our method offers improved regret upper bounds and does not require knowledge of graphical quantities. We conduct numerical experiments to demonstrate the computational efficiency and effectiveness of our approach in terms of regret upper bounds. These findings highlight the significance of our algorithm in advancing the field of stochastic contextual bandits with graph feedback, opening up avenues for practical applications in various domains.Comment: arXiv admin note: text overlap with arXiv:2010.03104 by other author

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

"Though Much is Taken" -- Reflections on Aging, Health, and Medical Care

The Medicare trust funds face huge prospective deficits by the end of this decade.This paper discusses trends in six areas that bear on the Medicare problem: the number of the elderly, their health status, use of medical care, labor force participation, income, and living arrangements. Among the most important findings are: 1) a very large increase since 1965 in life expectancy at age 65; 2) a very large increase since 1976 in real per capita health care expenditures on the elderly relative to expenditures on persons under age 65; 3) a cross-sectional increase with age in per capita health care expenditures that is primarily attributable to very large expenditures in the last year of life; 4) a sharp decrease in labor force participation and a sharp increase in relative income of the elderly since 1965.The paper concludes by raising questions about the need to reconsider the definition of the elderly, the need for more flexible labor market arrangements for older workers, and a need for a social consensus concerning appropriate care of dying patients.

Scale invariance in natural and artificial collective systems : a review

Self-organized collective coordinated behaviour is an impressive phenomenon, observed in a variety of natural and artificial systems, in which coherent global structures or dynamics emerge from local interactions between individual parts. If the degree of collective integration of a system does not depend on size, its level of robustness and adaptivity is typically increased and we refer to it as scale-invariant. In this review, we first identify three main types of self-organized scale-invariant systems: scale-invariant spatial structures, scale-invariant topologies and scale-invariant dynamics. We then provide examples of scale invariance from different domains in science, describe their origins and main features and discuss potential challenges and approaches for designing and engineering artificial systems with scale-invariant properties

Aspects of Credit Risk Modeling

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