Vaidotas Šimkus

I am a PhD student in Data Science at the School of Informatics of the University of Edinburgh, advised by Michael Gutmann.

My research is currently focused on developing principled methods for density estimation from incomplete data. My broader interests include deep density estimation, variational inference, deep learning, and software for machine learning.

I have obtained a MSc in Artificial Intelligence from the University of Edinburgh and a BEng in Software Engineering from the University of Southampton.

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Learning Job Titles Similarity from Noisy Skill Labels

Rabih Zbib, Lucas Alvarez Lacasa, Federico Retyk, Rus Poves, Juan Aizpuru, Hermenegildo Fabregat, Vaidotas Šimkus, Emilia García-Casademont
FEAST workshop at ECML-PKDD, 2022
url / bib / arxiv / dataset

We propose an unsupervised representation learning method for a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.

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Variational Gibbs inference for statistical model estimation from incomplete data

Vaidotas Šimkus, Ben Rhodes, Michael Gutmann
arXiv, 2021
bib / arxiv / code / demo

We propose a new method for statistical model estimation from incomplete data, called variational Gibbs inference (VGI). Whilst being general-pupose, the proposed method outperforms existing VAE and normalising flow specific methods.