Michalis K. Titsias

According to our database, Michalis K. Titsias authored at least 66 papers between 2000 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models.

Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias

CoRR, 2024

2023

Variance reduction for Metropolis-Hastings samplers.

Angelos Alexopoulos, Petros Dellaportas, Michalis K. Titsias

Stat. Comput., 2023

Kalman Filter for Online Classification of Non-Stationary Data.

Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein

CoRR, 2023

Optimal Preconditioning and Fisher Adaptive Langevin Sampling.

Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Sequential changepoint detection in neural networks with checkpoints.

Michalis K. Titsias, Jakub Sygnowski, Yutian Chen

Stat. Comput., 2022

Personalized Federated Learning with Exact Stochastic Gradient Descent.

Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos, Michalis K. Titsias

CoRR, 2022

Gradient Estimation with Discrete Stein Operators.

Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Information-theoretic Online Memory Selection for Continual Learning.

Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

Michalis K. Titsias, Jiaxin Shi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Large scale multi-label learning using Gaussian processes.

Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias

Mach. Learn., 2021

Information theoretic meta learning with Gaussian processes.

Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Unbiased gradient estimation for variational auto-encoders using coupled Markov chains.

Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Entropy-based adaptive Hamiltonian Monte Carlo.

Marcel Hirt, Michalis K. Titsias, Petros Dellaportas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Information Theoretic Meta Learning with Gaussian Processes.

Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov

CoRR, 2020

Functional Regularisation for Continual Learning with Gaussian Processes.

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

Proceedings of the 8th International Conference on Learning Representations, 2020

Sparse Orthogonal Variational Inference for Gaussian Processes.

Jiaxin Shi, Michalis K. Titsias, Andriy Mnih

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Prescribed Generative Adversarial Networks.

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

CoRR, 2019

Functional Regularisation for Continual Learning using Gaussian Processes.

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

CoRR, 2019

Gradient-based Adaptive Markov Chain Monte Carlo.

Michalis K. Titsias, Petros Dellaportas

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Contrastive Divergence for Combining Variational Inference and MCMC.

Francisco J. R. Ruiz, Michalis K. Titsias

Proceedings of the 36th International Conference on Machine Learning, 2019

Unbiased Implicit Variational Inference.

Michalis K. Titsias, Francisco J. R. Ruiz

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models.

Kaspar Märtens, Michalis K. Titsias, Christopher Yau

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules.

Michalis K. Titsias, Sotirios Nikoloutsopoulos

CoRR, 2018

Fully Scalable Gaussian Processes using Subspace Inducing Inputs.

Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias

CoRR, 2018

Augment and Reduce: Stochastic Inference for Large Categorical Distributions.

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

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Bayesian Boolean Matrix Factorisation.

Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes.

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

J. Mach. Learn. Res., 2016

Overdispersed Black-Box Variational Inference.

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

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.

Apostolos N. Adamakos, Michalis K. Titsias

Proceedings of the 9th Hellenic Conference on Artificial Intelligence, 2016

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

Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

The Generalized Reparameterization Gradient.

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

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling.

Merkourios Karaliopoulos, Iordanis Koutsopoulos, Michalis K. Titsias

Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing, 2016

2015

Local Expectation Gradients for Black Box Variational Inference.

Michalis K. Titsias, Miguel Lázaro-Gredilla

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.

Rémi Bardenet, Michalis K. Titsias

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.

Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls

IEEE Geosci. Remote. Sens. Lett., 2014

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

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

CoRR, 2014

Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models.

Michalis K. Titsias, Christopher Yau

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.

Michalis K. Titsias, Miguel Lázaro-Gredilla

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

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

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

CoRR, 2013

Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression.

Michalis K. Titsias, Miguel Lázaro-Gredilla

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.

Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls

Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013

2012

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.

Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray

BMC Syst. Biol., 2012

Manifold Relevance Determination.

Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning.

Michalis K. Titsias, Miguel Lázaro-Gredilla

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.

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

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.

Miguel Lázaro-Gredilla, Michalis K. Titsias

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Bayesian Gaussian Process Latent Variable Model.

Michalis K. Titsias, Neil D. Lawrence

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels.

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

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Gaussian Processes for Missing Species in Biochemical Systems.

Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias

Proceedings of the Learning and Inference in Computational Systems Biology., 2010

2009

Variational Learning of Inducing Variables in Sparse Gaussian Processes.

Michalis K. Titsias

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Web Page Rank Prediction with PCA and EM Clustering.

Polyxeni Zacharouli, Michalis K. Titsias, Michalis Vazirgiannis

Proceedings of the Algorithms and Models for the Web-Graph, 6th International Workshop, 2009

2008

Efficient Sampling for Gaussian Process Inference using Control Variables.

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

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

2007

The Infinite Gamma-Poisson Feature Model.

Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Bayesian Feature and Model Selection for Gaussian Mixture Models.

Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas

IEEE Trans. Pattern Anal. Mach. Intell., 2006

Sequential Learning of Layered Models from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the Toward Category-Level Object Recognition, 2006

2005

Unsupervised learning of multiple objects in images.

Michalis K. Titsias

PhD thesis, 2005

Unsupervised Learning of Multiple Aspects of Moving Objects from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the Advances in Informatics, 2005

Fast Learning of Sprites using Invariant Features.

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

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.

Christopher K. I. Williams, Michalis K. Titsias

Neural Comput., 2004

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2004

2003

Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing.

Michalis K. Titsias, Aristidis Likas

IEEE Trans. Pattern Anal. Mach. Intell., 2003

2002

Mixture of Experts Classification Using a Hierarchical Mixture Model.

Michalis K. Titsias, Aristidis Likas

Neural Comput., 2002

A Bayesian Regularization Method for the Probabilistic RBF Network.

Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas

Proceedings of the Methods and Applications of Artificial Intelligence, 2002

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

Christopher K. I. Williams, Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Shared kernel models for class conditional density estimation.

Michalis K. Titsias, Aristidis Likas

IEEE Trans. Neural Networks, 2001

2000

A Probabilistic RBF Network for Classification.

Michalis K. Titsias, Aristidis Likas

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000