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

Recent papers

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]

M. K. Titsias and C. Yau.
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models. NIPS, 27, 2014. [supplementary] [software coming soon]

M. K. Titsias and M. Lazaro-Gredilla.
Doubly Stochastic Variational Bayes for non-Conjugate Inference. 31st International Conference on Machine Learning (ICML), Beijing, China, 2014. [supplementary] [MATLAB software]

*Joint first author.

Office hours

My weekly office hours for this semester are: Tuesday 16:00-17:00 and Thursday 14:00-15:00.