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Featured researches published by Emmanouil Antonios Platanios.


IEEE Transactions on Pattern Analysis and Machine Intelligence | 2014

Gaussian Process-Mixture Conditional Heteroscedasticity

Emmanouil Antonios Platanios; Sotirios P. Chatzis

Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.


national conference on artificial intelligence | 2015

Never-ending learning

Tom M. Mitchell; William W. Cohen; E. Hruschka; Partha Pratim Talukdar; Justin Betteridge; Andrew Carlson; Bhavana Dalvi; Matt Gardner; Bryan Kisiel; Jayant Krishnamurthy; Ni Lao; Kathryn Mazaitis; T. Mohamed; Ndapandula Nakashole; Emmanouil Antonios Platanios; Alan Ritter; Mehdi Samadi; Burr Settles; Richard C. Wang; Derry Tanti Wijaya; Abhinav Gupta; Xi Chen; A. Saparov; M. Greaves; J. Welling


uncertainty in artificial intelligence | 2014

Estimating accuracy from unlabeled data

Emmanouil Antonios Platanios; Avrim Blum; Tom M. Mitchell


international conference on machine learning | 2016

Estimating accuracy from unlabeled data: a Bayesian approach

Emmanouil Antonios Platanios; Avinava Dubey; Tom M. Mitchell


neural information processing systems | 2017

Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

Emmanouil Antonios Platanios; Hoifung Poon; Tom M. Mitchell; Eric Horvitz


Theory and Applications of Categories | 2015

CMUML System for KBP 2015 Cold Start Slot Filling.

Bryan Kisiel; Bill McDowell; Matt Gardner; Ndapandula Nakashole; Emmanouil Antonios Platanios; Abulhair Saparov; Shashank Srivastava; Derry Tanti Wijaya; Tom M. Mitchell


arXiv: Learning | 2012

Mixture Gaussian Process Conditional Heteroscedasticity

Emmanouil Antonios Platanios; Sotirios P. Chatzis


Archive | 2012

A Mixture Gaussian Process Conditional Heteroscedasticity Model with Power-Law Nature

Emmanouil Antonios Platanios; Sotirios P. Chatzis


empirical methods in natural language processing | 2018

Contextual Parameter Generation for Universal Neural Machine Translation

Emmanouil Antonios Platanios; Mrinmaya Sachan; Graham Neubig; Tom M. Mitchell


arXiv: Learning | 2018

Agreement-based Learning.

Emmanouil Antonios Platanios

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Tom M. Mitchell

Carnegie Mellon University

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Bryan Kisiel

Carnegie Mellon University

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Sotirios P. Chatzis

Cyprus University of Technology

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Matt Gardner

Carnegie Mellon University

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A. Saparov

Carnegie Mellon University

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Abhinav Gupta

Carnegie Mellon University

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