ML Anthology

Titsias, Michalis K.

24 publications

NeurIPS 2024 Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
NeurIPS 2024 Simplified and Generalized Masked Diffusion for Discrete Data Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias
NeurIPS 2023 Optimal Preconditioning and Fisher Adaptive Langevin Sampling Michalis K. Titsias
NeurIPS 2022 Gradient Estimation with Discrete Stein Operators Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester W. Mackey
NeurIPS 2021 Entropy-Based Adaptive Hamiltonian Monte Carlo Marcel Hirt, Michalis K. Titsias, Petros Dellaportas
UAI 2021 Information Theoretic Meta Learning with Gaussian Processes Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov
MLJ 2021 Large Scale Multi-Label Learning Using Gaussian Processes Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
UAI 2021 Unbiased Gradient Estimation for Variational Auto-Encoders Using Coupled Markov Chains Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet
ICLR 2020 Functional Regularisation for Continual Learning with Gaussian Processes Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
AISTATS 2019 Unbiased Implicit Variational Inference Michalis K. Titsias, Francisco Ruiz
ICML 2017 Bayesian Boolean Matrix Factorisation Tammo Rukat, Chris C. Holmes, Michalis K. Titsias, Christopher Yau
UAI 2016 Overdispersed Black-Box Variational Inference Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
JMLR 2016 Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
ICML 2012 Manifold Relevance Determination Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence
NeurIPS 2011 Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning Michalis K. Titsias, Miguel Lázaro-Gredilla
NeurIPS 2011 Variational Gaussian Process Dynamical Systems Andreas Damianou, Michalis K. Titsias, Neil D. Lawrence
ICML 2011 Variational Heteroscedastic Gaussian Process Regression Miguel Lázaro-Gredilla, Michalis K. Titsias
NeurIPS 2008 Efficient Sampling for Gaussian Process Inference Using Control Variables Neil D. Lawrence, Magnus Rattray, Michalis K. Titsias
NeurIPS 2007 The Infinite Gamma-Poisson Feature Model Michalis K. Titsias
CVPR 2004 Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video Michalis K. Titsias, Christopher K. I. Williams
CVPRW 2004 Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video Michalis K. Titsias, Christopher K. I. Williams
NeCo 2004 Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning Christopher K. I. Williams, Michalis K. Titsias
NeurIPS 2002 Learning About Multiple Objects in Images: Factorial Learning Without Factorial Search Christopher K. I. Williams, Michalis K. Titsias
NeCo 2002 Mixture of Experts Classification Using a Hierarchical Mixture Model Michalis K. Titsias, Aristidis Likas