Michalis K. Titsias

According to our database1, Michalis K. Titsias authored at least 46 papers between 2000 and 2018.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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PhD thesis 
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On csauthors.net:

Bibliography

2018
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules.
CoRR, 2018

Unbiased Implicit Variational Inference.
CoRR, 2018

Fully Scalable Gaussian Processes using Subspace Inducing Inputs.
CoRR, 2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions.
CoRR, 2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Bayesian Boolean Matrix Factorisation.
CoRR, 2017

Bayesian Boolean Matrix Factorisation.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.
Journal of Machine Learning Research, 2016

Overdispersed Black-Box Variational Inference.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market.
Proceedings of the 9th Hellenic Conference on Artificial Intelligence, 2016

One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

The Generalized Reparameterization Gradient.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Local Expectation Gradients for Black Box Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Inference for determinantal point processes without spectral knowledge.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014
Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes.
IEEE Geosci. Remote Sensing Lett., 2014

Variational Inference for Uncertainty on the Inputs of Gaussian Process Models.
CoRR, 2014

Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Doubly Stochastic Variational Bayes for non-Conjugate Inference.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Statistical Inference in Hidden Markov Models using $k$-segment Constraints.
CoRR, 2013

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes.
Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013

2012
Manifold Relevance Determination
CoRR, 2012

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.
BMC Systems Biology, 2012

Manifold Relevance Determination.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Variational Gaussian Process Dynamical Systems
CoRR, 2011

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Variational Gaussian Process Dynamical Systems.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Variational Heteroscedastic Gaussian Process Regression.
Proceedings of the 28th International Conference on Machine Learning, 2011

2010
Bayesian Gaussian Process Latent Variable Model.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

2009
Variational Learning of Inducing Variables in Sparse Gaussian Processes.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Web Page Rank Prediction with PCA and EM Clustering.
Proceedings of the Algorithms and Models for the Web-Graph, 6th International Workshop, 2009

2008
Efficient Sampling for Gaussian Process Inference using Control Variables.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007
The Infinite Gamma-Poisson Feature Model.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006
Bayesian Feature and Model Selection for Gaussian Mixture Models.
IEEE Trans. Pattern Anal. Mach. Intell., 2006

Sequential Learning of Layered Models from Video.
Proceedings of the Toward Category-Level Object Recognition, 2006

2005
Unsupervised Learning of Multiple Aspects of Moving Objects from Video.
Proceedings of the Advances in Informatics, 2005

Fast Learning of Sprites using Invariant Features.
Proceedings of the British Machine Vision Conference 2005, Oxford, UK, September 2005, 2005

2004
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning.
Neural Computation, 2004

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2004

2003
Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing.
IEEE Trans. Pattern Anal. Mach. Intell., 2003

2002
Mixture of Experts Classification Using a Hierarchical Mixture Model.
Neural Computation, 2002

A Bayesian Regularization Method for the Probabilistic RBF Network.
Proceedings of the Methods and Applications of Artificial Intelligence, 2002

Learning About Multiple Objects in Images: Factorial Learning without Factorial Search.
Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001
Shared kernel models for class conditional density estimation.
IEEE Trans. Neural Networks, 2001

2000
A Probabilistic RBF Network for Classification.
IJCNN (4), 2000


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