Online Expert Mediators

Online Learning with Expert Automata

We consider a general framework of online learning with expert advice where the regret is defined with respect to a competitor class defined by a weighted automaton over sequences of experts. Our framework covers several problems previously studied, in particular that of competing against k-shifting experts. We give a series of algorithms for this problem, including an automata-based algorithm ...

Robotics & Embodied Systems Expert Online Consultation

Smooth Online Learning of Expert Advice

This paper is concerned with algorithms for online learning of expert advice and contains both theoretical and empirical results. In the first part of the paper we present new online algorithms for combining expert opinions. Unlike most previous algorithms, our algorithms “smoothly” adjust their learning rates without forgetting past performance of the experts. Our analysis show that the propos...

Integrated Expert Recommendation Model for Online Communities

Online communities have become vital places for Web 2.0 users to share knowledge and experiences. Recently, finding expertise user in community has become an important research issue. This paper proposes a novel cascaded model for expert recommendation using aggregated knowledge extracted from enormous contents and social network features. Vector space model is used to compute the relevance of ...

It's about time: Online Macrotask Sequencing in Expert Crowdsourcing

We introduce the problem of Task Assignment and Sequencing (TAS), which adds the timeline perspective to expert crowdsourcing optimization. Expert crowdsourcing involves macrotasks, like document writing, product design, or web development, which take more time than typical binary microtasks, require expert skills, assume varying degrees of knowledge over a topic, and require crowd workers to b...