Titsias, Michalis

17 publications

ICML 2025 Learning-Order Autoregressive Models with Application to Molecular Graph Generation Zhe Wang, Jiaxin Shi, Nicolas Heess, Arthur Gretton, Michalis Titsias

ICML 2025 New Bounds for Sparse Variational Gaussian Processes Michalis Titsias

NeurIPS 2025 Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited Thang D Bui, Michalis Titsias

ICLR 2024 Kalman Filter for Online Classification of Non-Stationary Data Michalis Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jorg Bornschein

NeurIPSW 2023 Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models Amal Rannen-Triki, Jorg Bornschein, Razvan Pascanu, Alexandre Galashov, Michalis Titsias, Marcus Hutter, András György, Yee Whye Teh

NeurIPSW 2023 Stochastic Linear Dynamics in Parameters to Deal with Neural Networks Plasticity Loss Alexandre Galashov, Michalis Titsias, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

AISTATS 2022 Double Control Variates for Gradient Estimation in Discrete Latent Variable Models Michalis Titsias, Jiaxin Shi

ICLR 2022 Information-Theoretic Online Memory Selection for Continual Learning Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis Titsias

AISTATS 2020 Sparse Orthogonal Variational Inference for Gaussian Processes Jiaxin Shi, Michalis Titsias, Andriy Mnih

ICML 2019 A Contrastive Divergence for Combining Variational Inference and MCMC Francisco Ruiz, Michalis Titsias

AISTATS 2019 Augmented Ensemble MCMC Sampling in Factorial Hidden Markov Models Kaspar Märtens, Michalis Titsias, Christopher Yau

NeurIPS 2019 Gradient-Based Adaptive Markov Chain Monte Carlo Michalis Titsias, Petros Dellaportas

ICML 2018 Augment and Reduce: Stochastic Inference for Large Categorical Distributions Francisco Ruiz, Michalis Titsias, Adji Bousso Dieng, David Blei

ICML 2014 Doubly Stochastic Variational Bayes for Non-Conjugate Inference Michalis Titsias, Miguel Lázaro-Gredilla

AISTATS 2010 Bayesian Gaussian Process Latent Variable Model Michalis Titsias, Neil D. Lawrence

AISTATS 2010 Efficient Multioutput Gaussian Processes Through Variational Inducing Kernels Mauricio Álvarez, David Luengo, Michalis Titsias, Neil D. Lawrence

AISTATS 2009 Variational Learning of Inducing Variables in Sparse Gaussian Processes Michalis Titsias