Michalis K. Titsias

Aristidis Likas, Michalis K. Titsias, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing

Aristidis Likas, Michalis K. Titsias, M. Titsias, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Overdispersed Black-Box Variational Inference

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

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models pdf

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

Mixture of Experts Classification Using a Hierarchical Mixture Model

Aristidis Likas, Michalis K. Titsias, M. Titsias, 2002, Neural Computation.

Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models

Christopher Yau, Michalis K. Titsias, C. Yau, 2014, NIPS.

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression

Miguel Lázaro-Gredilla, Michalis K. Titsias, 2013, NIPS.

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

Michalis K. Titsias, M. Titsias, 2016, NIPS.

Information Theoretic Meta Learning with Gaussian Processes pdf

Alexandre Galashov, Michalis K. Titsias, Sotirios Nikoloutsopoulos, 2020, ArXiv.

Fully Scalable Gaussian Processes using Subspace Inducing Inputs pdf

Petros Dellaportas, Michalis K. Titsias, Aristeidis Panos, 2018, ArXiv.

Unsupervised Learning of Multiple Aspects of Moving Objects from Video

Christopher K. I. Williams, Michalis K. Titsias, 2005, Panhellenic Conference on Informatics.

Fast Learning of Sprites using Invariant

Christopher K. I. Williams, Moray Allan, Michalis K. Titsias, 2005 .

Bayesian Boolean Matrix Factorisation pdf

Christopher Yau, Christopher C. Holmes, Michalis K. Titsias, 2017, ICML.

Shared kernel models for class conditional density estimation

Aristidis Likas, Michalis K. Titsias, M. Titsias, 2001, IEEE Trans. Neural Networks.

Sparse Orthogonal Variational Inference for Gaussian Processes

Andriy Mnih, Michalis K. Titsias, Jiaxin Shi, 2020, AISTATS.

Sequential Learning of Layered Models from Video

Christopher K. I. Williams, Michalis K. Titsias, 2006, Toward Category-Level Object Recognition.

Efficient Sampling for Gaussian Process Inference using Control Variables

Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias, 2008, NIPS.

Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models

Kaspar Märtens, Christopher Yau, Michalis K. Titsias, 2017, AISTATS.

Local Expectation Gradients for Black Box Variational Inference

Miguel Lázaro-Gredilla, Michalis K. Titsias, M. Titsias, 2015, NIPS.

Prior Knowledge and Sparse Methods for Convolved Multiple Outputs Gaussian Processes

Neil D. Lawrence, David Luengo, Michalis K. Titsias, 2009 .

Augment and Reduce: Stochastic Inference for Large Categorical Distributions pdf

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2018, ICML.

Variational Learning of Inducing Variables in Sparse Gaussian Processes

Michalis K. Titsias, M. Titsias, 2009, AISTATS.

Prescribed Generative Adversarial Networks pdf

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2019, ArXiv.

Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules pdf

Michalis K. Titsias, Sotirios Nikoloutsopoulos, M. Titsias, 2018, ArXiv.

Variational Gaussian Process Dynamical Systems

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

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

Arnaud Doucet, Francisco J. R. Ruiz, Michalis K. Titsias, 2020, ArXiv.

A Bayesian Regularization Method for the Probabilistic RBF Network

Aristidis Likas, Constantinos Constantinopoulos, Michalis K. Titsias, 2002, SETN.

Learning About Multiple Objects in Images: Factorial Learning without Factorial Search

Christopher K. I. Williams, Michalis K. Titsias, 2002, NIPS.

Functional Regularisation for Continual Learning using Gaussian Processes pdf

Yee Whye Teh, Razvan Pascanu, Jonathan Schwarz, 2019, ICLR.

Bayesian feature and model selection for Gaussian mixture models

Aristidis Likas, Constantinos Constantinopoulos, Michalis K. Titsias, 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Inference for determinantal point processes without spectral knowledge

Rémi Bardenet, Michalis K. Titsias, 2015, NIPS.

Web Page Rank Prediction with PCA and EM Clustering

Michalis Vazirgiannis, Michalis K. Titsias, Polyxeni Zacharouli, 2009, WAW.

Doubly Stochastic Variational Bayes for non-Conjugate Inference

Miguel Lázaro-Gredilla, Michalis K. Titsias, M. Titsias, 2014, ICML.

Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes

Gustavo Camps-Valls, Miguel Lázaro-Gredilla, Jochem Verrelst, 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

The Infinite Gamma-Poisson Feature Model

Michalis K. Titsias, M. Titsias, 2007, NIPS.

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes

Neil D. Lawrence, Michalis K. Titsias, Andreas C. Damianou, 2016, J. Mach. Learn. Res..

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

Arnaud Doucet, Francisco J. R. Ruiz, Michalis K. Titsias, 2021, UAI.

Large scale multi-label learning using Gaussian processes

Petros Dellaportas, Michalis K. Titsias, Aristeidis Panos, 2021, Machine Learning.

Entropy-based adaptive Hamiltonian Monte Carlo

Marcel Hirt, Petros Dellaportas, Michalis K. Titsias, 2021, NeurIPS.

Double Control Variates for Gradient Estimation in Discrete Latent Variable Models pdf

Michalis K. Titsias, Jiaxin Shi, 2021, ArXiv.

Gaussian Processes for Missing Species in Biochemical Systems

Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias, 2010, Learning and Inference in Computational Systems Biology.

Auxiliary gradient‐based sampling algorithms

Omiros Papaspiliopoulos, Michalis K. Titsias, O. Papaspiliopoulos, 2016, 1610.09641.

Scalable inference for a full multivariate stochastic volatility model

Michalis K. Titsias, P. Dellaportas, A. Plataniotis, 2015, Journal of Econometrics.

Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning

Christopher K. I. Williams, Michalis K. Titsias, 2004, Neural Computation.

Information-theoretic Online Memory Selection for Continual Learning

Michalis K. Titsias, Daniele Calandriello, M. Titsias, 2022, ICLR.

Variational Model Selection for Sparse Gaussian Process Regression

Michalis K. Titsias, M. Titsias, 2008 .

Unsupervised learning of multiple objects in images

Michalis Titsias, Michalis K. Titsias, M. Titsias, 2005 .

matlab toolbox for learning object models from video

Michalis K. Titsias, 2005 .

Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC distributions

Michalis K. Titsias, M. Titsias, 2017, 1708.01529.

Bayesian Time Series Models: Markov chain Monte Carlo algorithms for Gaussian processes

Michalis K. Titsias, Neil D. Lawrence, M. Rattray, 2011 .

Kalman Filter for Online Classification of Non-Stationary Data

Michalis K. Titsias, Y. Teh, Razvan Pascanu, 2023, ArXiv.

Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning

Michalis K. Titsias, Christopher K I Williams, 2022 .