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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Journal ArticleDOI
Computational design of thermally stable and precipitation-hardened Al-Co-Cr-Fe-Ni-Ti high entropy alloys
Jithin Joseph,Manisha Senadeera,Qi Chao,Karl F. Shamlaye,Santu Rana,Sunil Gupta,Svetha Venkatesh,Peter Hodgson,Matthew Barnett,Daniel Fabijanic +9 more
TL;DR: In this article, a multi-dimensional Al-Co-Cr-Fe-Ni-Ti alloy space with a Ni3(Al, Ti)-type ordered (γ') phase in a disordered face-centered cubic matrix phase (γ) without detrimental intermetallic phases at 800°C was obtained by integrating calculated-phase diagrams and a computational framework.
Proceedings Article
Bayesian Optimization with Unknown Search Space
TL;DR: This work proposes a systematic volume expansion strategy for the Bayesian optimization to guarantee that in iterative expansions of the search space, the method can find a point whose function value within epsilon of the objective function maximum.
Posted Content
Bayesian Optimization with Unknown Search Space
TL;DR: In this paper, a systematic volume expansion strategy for the Bayesian optimization is proposed to guarantee that in iterative expansions of the search space, the method can find a point whose function value within epsilon of the objective function maximum.
Journal ArticleDOI
Stabilizing High-Dimensional Prediction Models Using Feature Graphs
TL;DR: Using a cohort of patients with heart failure, this work demonstrates better feature stability and goodness-of-fit through feature graph stabilization through Laplacian-based regularization into a regression model.
Book ChapterDOI
Trans2Vec: learning transaction embedding via items and frequent itemsets
TL;DR: This paper proposes an unsupervised method which learns low-dimensional continuous vectors for transactions based on information of both singleton items and FIs and demonstrates the superior performance of the proposed method in classifying transactions on four datasets compared with several state-of-the-art baselines.