I am Assistant Professor at the Department of Informatics in Athens University of Economics and Business (AUEB). I am interested in the theory and applications of Machine Learning, Deep Learning, Bayesian Statistics and Data Science. My CV can be found here.

Recent preprints

M. K. Titsias.
Learning Model Reparametrizations: Implicit Variational Inference by Fitting MCMC Distributions, arXiv:1708.01529 , 2017.

K. Märtens, M. K Titsias, C. Yau.
Rejection-free Ensemble MCMC with applications to Factorial Hidden Markov Models, arXiv:1703.08520, 2017.

P. Dellaportas, A. Plataniotis, M. K. Titsias.
Scalable inference for a full multivariate stochastic volatility model, arXiv:1510.05257, 2015.

Recent conference and journal papers

(New) F. J. R. Ruiz, M. K. Titsias, A. B. Dieng, and D. M. Blei.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions, Proceedings of the 35th International Conference on Machine Learning (ICML), 2018.

(New) M. K. Titsias and O. Papaspiliopoulos.
Auxiliary gradient-based sampling algorithms, Journal of the Royal Statistical Society: Series B, to appear. [slides]

T. Rukat, C. C. Holmes, M. K. Titsias, C. Yau.
Bayesian Boolean Matrix Factorisation, Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

M. K. Titsias.
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities, NIPS, 29, 2016.

F. J. R. Ruiz, M. K. Titsias and D. M. Blei.
The Generalized Reparameterization Gradient, NIPS, 29, 2016.

M. K. Titsias, and C. Yau.
The Hamming Ball Sampler. Journal of the American Statistical Association (JASA), Theory and Methods, to appear.

F. J. R. Ruiz, M. K. Titsias and D. M. Blei.
Overdispersed Black-Box Variational Inference, Uncertainty in Artificial Intelligence (UAI), 2016. [supplementary]

M. Karaliopoulos, I. Koutsopoulos and M. K. Titsias.
First Learn then Earn: Optimizing Mobile Crowdsensing campaigns through data-driven user profiling, Proceedings of ACM International Symposium on Mobile Ad-Hoc Networking and Computing (Mobihoc), 2016.

M. K. Titsias and M. Lazaro-Gredilla.
Local Expectation Gradients for Black Box Variational Inference, NIPS, 28, 2015. [supplementary] [sigmoid belief net code]

R. Bardenet* and M. K. Titsias*.
Inference for determinantal point processes without spectral knowledge. NIPS, 28, 2015.

A. Damianou*, M. K. Titsias* and N. Lawrence.
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. Journal of Machine Learning Research (JMLR), 17(42):1-62, 2016. [MATLAB code]

M. K. Titsias, C. C. Holmes and C. Yau.
Statistical Inference in Hidden Markov Models using k-segment constraints. Journal of the American Statistical Association (JASA), Theory and Methods, 111(513):200-215, 2016. [software coming soon]

*Joint first author.

Office hours

My weekly office hours for this semester are: Thursday 14:00-16:00.