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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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Auxiliary gradient-based sampling algorithms


Jan 25, 2018
Michalis K. Titsias, Omiros Papaspiliopoulos

* 41 pages, 8 figures, 11 tables, To appear in Journal of the Royal Statistical Society: Series B 

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Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions


Aug 04, 2017
Michalis K. Titsias

* 16 pages, 6 figures 

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Bayesian Boolean Matrix Factorisation


Feb 25, 2017
Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau


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One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities


Oct 29, 2016
Michalis K. Titsias

* To appear in NIPS 2016 

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The Generalized Reparameterization Gradient


Oct 19, 2016
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

* 16 pages, 15 figures, NIPS version 

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Overdispersed Black-Box Variational Inference


Mar 03, 2016
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei

* 10 pages, 6 figures 

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Inference for determinantal point processes without spectral knowledge


Jul 04, 2015
Rémi Bardenet, Michalis K. Titsias


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Local Expectation Gradients for Doubly Stochastic Variational Inference


Mar 04, 2015
Michalis K. Titsias


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Variational Inference for Uncertainty on the Inputs of Gaussian Process Models


Sep 08, 2014
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

* 51 pages (of which 10 is Appendix), 19 figures 

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Statistical Inference in Hidden Markov Models using $k$-segment Constraints


Nov 05, 2013
Michalis K. Titsias, Christopher Yau, Christopher C. Holmes

* 37 pages 

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Variational Gaussian Process Dynamical Systems


Jul 25, 2011
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence

* 16 pages, 19 figures 

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Variational Inducing Kernels for Sparse Convolved Multiple Output Gaussian Processes


Dec 16, 2009
Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence

* Technical report, 22 pages, 8 figures 

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