S
Svetha Venkatesh
Researcher at Deakin University
Publications - 864
Citations - 20118
Svetha Venkatesh is an academic researcher from Deakin University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 60, co-authored 828 publications receiving 16441 citations. Previous affiliations of Svetha Venkatesh include Australian National University & National University of Singapore.
Papers
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Proceedings ArticleDOI
Flickr hypergroups
TL;DR: A novel approach to group searching through hypergroup discovery is proposed, starting from roughly 11,000 Flickr groups' content and membership information, and it is shown that hypergroups found are generally consistent and can be described through topic-based and similarity-based measures.
Proceedings Article
Learning other agents' preferences in multiagent negotiation
TL;DR: The integration of a learning module into a communication-intensive negotiating agent architecture gives the agents the ability to learn about other agents' preferences via past interactions, which allows them to make better coordinated decisions.
Proceedings ArticleDOI
TOBY: early intervention in autism through technology
TL;DR: TOBY Playpad is described, an early intervention program for children with Autism Spectrum Disorder (ASD) that teaches the teacher -- the parent -- during the crucial period following diagnosis, which often coincides with no access to formal therapy.
Proceedings Article
Ordinal Boltzmann Machines for collaborative filtering
Dinh Phung,Svetha Venkatesh +1 more
TL;DR: In this article, the authors explore and extend a probabilistic model known as Boltzmann Machine for collaborative filtering tasks, which seamlessly integrates both the similarity and co-occurrence in a principled manner.
Proceedings ArticleDOI
Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden Semi-Markov Model
TL;DR: This paper discusses the state-of-the-art duration modeling choices and addresses a large class of exponential family distributions to model state durations and investigates both discrete and continuous distributions from the exponential family for the problem of learning and recognizing ADLs.