Michalis K. Titsias

Sequential Changepoint Detection in Neural Networks with Checkpoints

Oct 06, 2020
Michalis K. Titsias, Jakub Sygnowski, Yutian Chen

* 17 pages, 7 figures

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Information Theoretic Meta Learning with Gaussian Processes

Oct 05, 2020
Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov

* 26 pages, 5 figures

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Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains

Oct 05, 2020
Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet

* 16 pages, 4 figures

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Gradient-based Adaptive Markov Chain Monte Carlo

Nov 04, 2019
Michalis K. Titsias, Petros Dellaportas

* 17 pages, 7 Figures, NeurIPS 2019

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Sparse Orthogonal Variational Inference for Gaussian Processes

Oct 24, 2019
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih

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Prescribed Generative Adversarial Networks

Oct 09, 2019
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias

* Code for this paper can be found at https://github.com/adjidieng/PresGANs

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A Contrastive Divergence for Combining Variational Inference and MCMC

May 28, 2019
Francisco J. R. Ruiz, Michalis K. Titsias

* International Conference on Machine Learning (ICML 2019). 12 pages, 3 figures

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Functional Regularisation for Continual Learning using Gaussian Processes

Jan 31, 2019
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh

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Unbiased Implicit Variational Inference

Oct 10, 2018
Michalis K. Titsias, Francisco J. R. Ruiz

* 9 pages, 3 figures

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Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules

Sep 30, 2018
Michalis K. Titsias, Sotirios Nikoloutsopoulos

* 11 pages, 2 figures

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Fully Scalable Gaussian Processes using Subspace Inducing Inputs

Jul 12, 2018
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias

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Augment and Reduce: Stochastic Inference for Large Categorical Distributions

Jun 07, 2018
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei

* Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, and David M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018
* 11 pages, 2 figures

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