Michalis K. Titsias

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 47

Top Topics

Gaussian Process

Top Venues

Publications

Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias
Simplified and Generalized Masked Diffusion for Discrete Data. CoRR (2024)
Siran Liu, Petros Dellaportas, Michalis K. Titsias
Can independent Metropolis beat crude Monte Carlo? CoRR (2024)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. ICLR (2024)
Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR (2024)
Angelos Alexopoulos, Petros Dellaportas, Michalis K. Titsias
Variance reduction for Metropolis-Hastings samplers. Stat. Comput. 33 (1) (2023)
Michalis K. Titsias
Optimal Preconditioning and Fisher Adaptive Langevin Sampling. NeurIPS (2023)
Michalis K. Titsias
Optimal Preconditioning and Fisher Adaptive Langevin Sampling. CoRR (2023)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. CoRR (2023)
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey
Gradient Estimation with Discrete Stein Operators. CoRR (2022)
Michalis K. Titsias, Jakub Sygnowski, Yutian Chen
Sequential changepoint detection in neural networks with checkpoints. Stat. Comput. 32 (2) (2022)
Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos, Michalis K. Titsias
Personalized Federated Learning with Exact Stochastic Gradient Descent. CoRR (2022)
Michalis K. Titsias, Jiaxin Shi
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. AISTATS (2022)
Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
Information-theoretic Online Memory Selection for Continual Learning. ICLR (2022)
Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias
Information-theoretic Online Memory Selection for Continual Learning. CoRR (2022)
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey
Gradient Estimation with Discrete Stein Operators. NeurIPS (2022)
Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains. UAI (2021)
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov
Information theoretic meta learning with Gaussian processes. UAI (2021)
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
Large scale multi-label learning using Gaussian processes. Mach. Learn. 110 (5) (2021)
Marcel Hirt, Michalis K. Titsias, Petros Dellaportas
Entropy-based adaptive Hamiltonian Monte Carlo. NeurIPS (2021)
Michalis K. Titsias, Jiaxin Shi
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. CoRR (2021)
Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. CoRR (2020)
Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov
Information Theoretic Meta Learning with Gaussian Processes. CoRR (2020)
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih
Sparse Orthogonal Variational Inference for Gaussian Processes. AISTATS (2020)
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
Functional Regularisation for Continual Learning with Gaussian Processes. ICLR (2020)
Michalis K. Titsias, Jakub Sygnowski, Yutian Chen
Sequential Changepoint Detection in Neural Networks with Checkpoints. CoRR (2020)
Michalis K. Titsias, Petros Dellaportas
Gradient-based Adaptive Markov Chain Monte Carlo. CoRR (2019)
Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias
Prescribed Generative Adversarial Networks. CoRR (2019)
Michalis K. Titsias, Francisco J. R. Ruiz
Unbiased Implicit Variational Inference. AISTATS (2019)
Francisco J. R. Ruiz, Michalis K. Titsias
A Contrastive Divergence for Combining Variational Inference and MCMC. CoRR (2019)
Kaspar Märtens, Michalis K. Titsias, Christopher Yau
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. AISTATS (2019)
Michalis K. Titsias, Petros Dellaportas
Gradient-based Adaptive Markov Chain Monte Carlo. NeurIPS (2019)
Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh
Functional Regularisation for Continual Learning using Gaussian Processes. CoRR (2019)
Jiaxin Shi, Michalis K. Titsias, Andriy Mnih
Sparse Orthogonal Variational Inference for Gaussian Processes. CoRR (2019)
Francisco J. R. Ruiz, Michalis K. Titsias
A Contrastive Divergence for Combining Variational Inference and MCMC. ICML (2019)
Michalis K. Titsias, Francisco J. R. Ruiz
Unbiased Implicit Variational Inference. CoRR (2018)
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. ICML (2018)
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
Fully Scalable Gaussian Processes using Subspace Inducing Inputs. CoRR (2018)
Michalis K. Titsias, Sotirios Nikoloutsopoulos
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules. CoRR (2018)
Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. CoRR (2018)
Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau
Bayesian Boolean Matrix Factorisation. ICML (2017)
Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau
Bayesian Boolean Matrix Factorisation. CoRR (2017)
Michalis K. Titsias
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities. NIPS (2016)
Apostolos N. Adamakos, Michalis K. Titsias
Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market. SETN (2016)
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
The Generalized Reparameterization Gradient. NIPS (2016)
Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei
Overdispersed Black-Box Variational Inference. UAI (2016)
Merkourios Karaliopoulos, Iordanis Koutsopoulos, Michalis K. Titsias
First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling. MobiHoc (2016)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. J. Mach. Learn. Res. 17 (2016)
Rémi Bardenet, Michalis K. Titsias
Inference for determinantal point processes without spectral knowledge. NIPS (2015)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Local Expectation Gradients for Black Box Variational Inference. NIPS (2015)
Michalis K. Titsias, Christopher Yau
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models. NIPS (2014)
Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls
Retrieval of Biophysical Parameters With Heteroscedastic Gaussian Processes. IEEE Geosci. Remote. Sens. Lett. 11 (4) (2014)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR (2014)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Doubly Stochastic Variational Bayes for non-Conjugate Inference. ICML (2014)
Michalis K. Titsias, Christopher Yau, Christopher C. Holmes
Statistical Inference in Hidden Markov Models using $k$-segment Constraints. CoRR (2013)
Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls
Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes. IGARSS (2013)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. NIPS (2013)
Michalis K. Titsias, Antti Honkela, Neil D. Lawrence, Magnus Rattray
Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison. BMC Syst. Biol. 6 (2012)
Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence
Manifold Relevance Determination CoRR (2012)
Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence
Manifold Relevance Determination. ICML (2012)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Gaussian Process Dynamical Systems CoRR (2011)
Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence
Variational Gaussian Process Dynamical Systems. NIPS (2011)
Miguel Lázaro-Gredilla, Michalis K. Titsias
Variational Heteroscedastic Gaussian Process Regression. ICML (2011)
Michalis K. Titsias, Miguel Lázaro-Gredilla
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. NIPS (2011)
Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. AISTATS (2010)
Michalis K. Titsias, Neil D. Lawrence
Bayesian Gaussian Process Latent Variable Model. AISTATS (2010)
Michalis K. Titsias
Variational Learning of Inducing Variables in Sparse Gaussian Processes. AISTATS (2009)
Polyxeni Zacharouli, Michalis K. Titsias, Michalis Vazirgiannis
Web Page Rank Prediction with PCA and EM Clustering. WAW (2009)
Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray
Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS (2008)
Michalis K. Titsias
The Infinite Gamma-Poisson Feature Model. NIPS (2007)
Michalis K. Titsias, Christopher K. I. Williams
Sequential Learning of Layered Models from Video. Toward Category-Level Object Recognition (2006)
Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas
Bayesian Feature and Model Selection for Gaussian Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 28 (6) (2006)
Michalis K. Titsias, Christopher K. I. Williams
Unsupervised Learning of Multiple Aspects of Moving Objects from Video. Panhellenic Conference on Informatics (2005)
Moray Allan, Michalis K. Titsias, Christopher K. I. Williams
Fast Learning of Sprites using Invariant Features. BMVC (2005)
Christopher K. I. Williams, Michalis K. Titsias
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. Neural Comput. 16 (5) (2004)
Michalis K. Titsias, Christopher K. I. Williams
Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video. CVPR Workshops (2004)
Michalis K. Titsias, Aristidis Likas
Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing. IEEE Trans. Pattern Anal. Mach. Intell. 25 (7) (2003)
Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas
A Bayesian Regularization Method for the Probabilistic RBF Network. SETN (2002)
Michalis K. Titsias, Aristidis Likas
Mixture of Experts Classification Using a Hierarchical Mixture Model. Neural Comput. 14 (9) (2002)
Christopher K. I. Williams, Michalis K. Titsias
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. NIPS (2002)
Michalis K. Titsias, Aristidis Likas
Shared kernel models for class conditional density estimation. IEEE Trans. Neural Networks 12 (5) (2001)
Michalis K. Titsias, Aristidis Likas
A Probabilistic RBF Network for Classification. IJCNN (4) (2000)