Recent publications

M. K. Titsias, A. Galashov, A. Rannen-Triki, R. Pascanu, Y. W. Teh and J. Bornschein. Kalman Filter for Online Classification of Non-Stationary Data. 12th International Conference on Learning Representations (ICLR), 2024.

M. K. Titsias. Optimal Preconditioning and Fisher Adaptive Langevin Sampling. Neural Information Processing Systems (NeurIPS), 2023.

A. Alexopoulos, P. Dellaportas and M. K. Titsias. Variance reduction for Metropolis–Hastings samplers. Statistics and Computing 33, 6 (2023).

J. Shi, Y. Zhou, J. Hwang, M. K. Titsias and L. Mackey. Gradient Estimation with Discrete Stein Operators. Neural Information Processing Systems (NeurIPS), 2022. NeurIPS 2022 Outstanding Paper Award

M. K. Titsias and J. Shi. Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. 25rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.

S. Sun, D. Calandriello, H. Hu, A. Li and M. K. Titsias. Information-theoretic Online Memory Selection for Continual Learning. International Conference on Learning Representations (ICLR), 2022.

M. Hirt, M. K. Titsias, P. Dellaportas. Entropy-based adaptive Hamiltonian Monte Carlo. Neural Information Processing Systems (NeurIPS), 2021.

M. K. Titsias, F. J. R. Ruiz, S. Nikoloutsopoulos and A. Galashov. Information Theoretic Meta Learning with Gaussian Processes. 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.

F. J. R. Ruiz, M. K. Titsias, T. Cemgil and A. Doucet. Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. 37th Conference on Uncertainty in Artificial Intelligence (UAI), 2021.

A. Panos, P. Dellaportas and M. K. Titsias. Large scale multi-label learning using Gaussian processes. Machine Learning, Volume 110, 965–987, 2021.

M. K. Titsias, J. Sygnowski, Y. Chen. Sequential Changepoint Detection in Neural Networks with Checkpoints. arXiv. 2020.