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ORCIDMichalis K. Titsias
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2020 – today
- 2024
[c44]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- [i26]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 abs/2403.01518 (2024) - [i25]Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias:
Simplified and Generalized Masked Diffusion for Discrete Data. CoRR abs/2406.04329 (2024) - [i24]Siran Liu, Petros Dellaportas, Michalis K. Titsias:
Can independent Metropolis beat crude Monte Carlo? CoRR abs/2406.17699 (2024) - 2023
- [j11]Angelos Alexopoulos
, Petros Dellaportas, Michalis K. Titsias:
Variance reduction for Metropolis-Hastings samplers. Stat. Comput. 33(1): 6 (2023) - [c43]Michalis K. Titsias:
Optimal Preconditioning and Fisher Adaptive Langevin Sampling. NeurIPS 2023 - [i23]Michalis K. Titsias:
Optimal Preconditioning and Fisher Adaptive Langevin Sampling. CoRR abs/2305.14442 (2023) - [i22]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 abs/2306.08448 (2023) - 2022
- [j10]Michalis K. Titsias, Jakub Sygnowski, Yutian Chen:
Sequential changepoint detection in neural networks with checkpoints. Stat. Comput. 32(2): 26 (2022) - [c42]Michalis K. Titsias, Jiaxin Shi:
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. AISTATS 2022: 6134-6151 - [c41]Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias:
Information-theoretic Online Memory Selection for Continual Learning. ICLR 2022 - [c40]Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey:
Gradient Estimation with Discrete Stein Operators. NeurIPS 2022 - [i21]Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K. Titsias, Lester Mackey:
Gradient Estimation with Discrete Stein Operators. CoRR abs/2202.09497 (2022) - [i20]Sotirios Nikoloutsopoulos, Iordanis Koutsopoulos, Michalis K. Titsias:
Personalized Federated Learning with Exact Stochastic Gradient Descent. CoRR abs/2202.09848 (2022) - [i19]Shengyang Sun, Daniele Calandriello, Huiyi Hu, Ang Li, Michalis K. Titsias:
Information-theoretic Online Memory Selection for Continual Learning. CoRR abs/2204.04763 (2022) - 2021
- [j9]Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias:
Large scale multi-label learning using Gaussian processes. Mach. Learn. 110(5): 965-987 (2021) - [c39]Marcel Hirt, Michalis K. Titsias, Petros Dellaportas:
Entropy-based adaptive Hamiltonian Monte Carlo. NeurIPS 2021: 28482-28495 - [c38]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: 707-717 - [c37]Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov:
Information theoretic meta learning with Gaussian processes. UAI 2021: 1597-1606 - [i18]Michalis K. Titsias, Jiaxin Shi:
Double Control Variates for Gradient Estimation in Discrete Latent Variable Models. CoRR abs/2111.05300 (2021) - 2020
- [c36]Jiaxin Shi, Michalis K. Titsias, Andriy Mnih:
Sparse Orthogonal Variational Inference for Gaussian Processes. AISTATS 2020: 1932-1942 - [c35]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 - [i17]Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov:
Information Theoretic Meta Learning with Gaussian Processes. CoRR abs/2009.03228 (2020) - [i16]Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. CoRR abs/2010.01845 (2020) - [i15]Michalis K. Titsias, Jakub Sygnowski, Yutian Chen:
Sequential Changepoint Detection in Neural Networks with Checkpoints. CoRR abs/2010.03053 (2020)
2010 – 2019
- 2019
- [c34]Michalis K. Titsias, Francisco J. R. Ruiz:
Unbiased Implicit Variational Inference. AISTATS 2019: 167-176 - [c33]Kaspar Märtens, Michalis K. Titsias, Christopher Yau:
Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models. AISTATS 2019: 2359-2367 - [c32]Francisco J. R. Ruiz, Michalis K. Titsias:
A Contrastive Divergence for Combining Variational Inference and MCMC. ICML 2019: 5537-5545 - [c31]Michalis K. Titsias, Petros Dellaportas:
Gradient-based Adaptive Markov Chain Monte Carlo. NeurIPS 2019: 15704-15713 - [i14]Michalis K. Titsias, Jonathan Schwarz, Alexander G. de G. Matthews, Razvan Pascanu, Yee Whye Teh:
Functional Regularisation for Continual Learning using Gaussian Processes. CoRR abs/1901.11356 (2019) - [i13]Francisco J. R. Ruiz, Michalis K. Titsias:
A Contrastive Divergence for Combining Variational Inference and MCMC. CoRR abs/1905.04062 (2019) - [i12]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias:
Prescribed Generative Adversarial Networks. CoRR abs/1910.04302 (2019) - [i11]Jiaxin Shi, Michalis K. Titsias, Andriy Mnih:
Sparse Orthogonal Variational Inference for Gaussian Processes. CoRR abs/1910.10596 (2019) - [i10]Michalis K. Titsias, Petros Dellaportas:
Gradient-based Adaptive Markov Chain Monte Carlo. CoRR abs/1911.01373 (2019) - 2018
- [c30]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. ICML 2018: 4400-4409 - [i9]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. CoRR abs/1802.04220 (2018) - [i8]Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias:
Fully Scalable Gaussian Processes using Subspace Inducing Inputs. CoRR abs/1807.02537 (2018) - [i7]Michalis K. Titsias, Francisco J. R. Ruiz:
Unbiased Implicit Variational Inference. CoRR abs/1808.02078 (2018) - [i6]Michalis K. Titsias, Sotirios Nikoloutsopoulos:
Bayesian Transfer Reinforcement Learning with Prior Knowledge Rules. CoRR abs/1810.00468 (2018) - 2017
- [c29]Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau:
Bayesian Boolean Matrix Factorisation. ICML 2017: 2969-2978 - [i5]Tammo Rukat, Christopher C. Holmes, Michalis K. Titsias, Christopher Yau:
Bayesian Boolean Matrix Factorisation. CoRR abs/1702.06166 (2017) - 2016
- [j8]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: 42:1-42:62 (2016) - [c28]Merkourios Karaliopoulos, Iordanis Koutsopoulos, Michalis K. Titsias:
First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling. MobiHoc 2016: 271-280 - [c27]Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
The Generalized Reparameterization Gradient. NIPS 2016: 460-468 - [c26]Michalis K. Titsias:
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities. NIPS 2016: 4161-4169 - [c25]Apostolos N. Adamakos, Michalis K. Titsias:
Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market. SETN 2016: 15:1-15:6 - [c24]Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
Overdispersed Black-Box Variational Inference. UAI 2016 - 2015
- [c23]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Local Expectation Gradients for Black Box Variational Inference. NIPS 2015: 2638-2646 - [c22]Rémi Bardenet, Michalis K. Titsias:
Inference for determinantal point processes without spectral knowledge. NIPS 2015: 3393-3401 - 2014
- [j7]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): 838-842 (2014) - [c21]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Doubly Stochastic Variational Bayes for non-Conjugate Inference. ICML 2014: 1971-1979 - [c20]Michalis K. Titsias, Christopher Yau:
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models. NIPS 2014: 2960-2968 - [i4]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR abs/1409.2287 (2014) - 2013
- [c19]Miguel Lázaro-Gredilla, Michalis K. Titsias, Jochem Verrelst, Gustavo Camps-Valls:
Estimation of vegetation chlorophyll content with Variational Heteroscedastic Gaussian Processes. IGARSS 2013: 3010-3013 - [c18]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression. NIPS 2013: 279-287 - [i3]Michalis K. Titsias, Christopher Yau, Christopher C. Holmes:
Statistical Inference in Hidden Markov Models using $k$-segment Constraints. CoRR abs/1311.1189 (2013) - 2012
- [j6]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: 53 (2012) - [c17]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. ICML 2012 - [i2]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. CoRR abs/1206.4610 (2012) - 2011
- [c16]Miguel Lázaro-Gredilla, Michalis K. Titsias:
Variational Heteroscedastic Gaussian Process Regression. ICML 2011: 841-848 - [c15]Michalis K. Titsias, Miguel Lázaro-Gredilla:
Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning. NIPS 2011: 2339-2347 - [c14]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. NIPS 2011: 2510-2518 - [i1]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. CoRR abs/1107.4985 (2011) - 2010
- [c13]Mauricio A. Álvarez, David Luengo, Michalis K. Titsias, Neil D. Lawrence:
Efficient Multioutput Gaussian Processes through Variational Inducing Kernels. AISTATS 2010: 25-32 - [c12]Michalis K. Titsias, Neil D. Lawrence:
Bayesian Gaussian Process Latent Variable Model. AISTATS 2010: 844-851 - [p1]Neil D. Lawrence, Magnus Rattray, Pei Gao, Michalis K. Titsias:
Gaussian Processes for Missing Species in Biochemical Systems. Learning and Inference in Computational Systems Biology 2010: 231-252
2000 – 2009
- 2009
- [c11]Polyxeni Zacharouli, Michalis K. Titsias, Michalis Vazirgiannis:
Web Page Rank Prediction with PCA and EM Clustering. WAW 2009: 104-115 - [c10]Michalis K. Titsias:
Variational Learning of Inducing Variables in Sparse Gaussian Processes. AISTATS 2009: 567-574 - 2008
- [c9]Michalis K. Titsias, Neil D. Lawrence, Magnus Rattray:
Efficient Sampling for Gaussian Process Inference using Control Variables. NIPS 2008: 1681-1688 - 2007
- [c8]Michalis K. Titsias:
The Infinite Gamma-Poisson Feature Model. NIPS 2007: 1513-1520 - 2006
- [j5]Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas:
Bayesian Feature and Model Selection for Gaussian Mixture Models. IEEE Trans. Pattern Anal. Mach. Intell. 28(6): 1013-1018 (2006) - [c7]Michalis K. Titsias, Christopher K. I. Williams:
Sequential Learning of Layered Models from Video. Toward Category-Level Object Recognition 2006: 577-595 - 2005
- [b1]Michalis K. Titsias:
Unsupervised learning of multiple objects in images. University of Edinburgh, UK, 2005 - [c6]Moray Allan, Michalis K. Titsias, Christopher K. I. Williams:
Fast Learning of Sprites using Invariant Features. BMVC 2005 - [c5]Michalis K. Titsias, Christopher K. I. Williams:
Unsupervised Learning of Multiple Aspects of Moving Objects from Video. Panhellenic Conference on Informatics 2005: 746-756 - 2004
- [j4]Christopher K. I. Williams, Michalis K. Titsias:
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. Neural Comput. 16(5): 1039-1062 (2004) - [c4]Michalis K. Titsias, Christopher K. I. Williams:
Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video. CVPR Workshops 2004: 179 - 2003
- [j3]Michalis K. Titsias, Aristidis Likas:
Class Conditional Density Estimation Using Mixtures with Constrained Component Sharing. IEEE Trans. Pattern Anal. Mach. Intell. 25(7): 924-928 (2003) - 2002
- [j2]Michalis K. Titsias, Aristidis Likas:
Mixture of Experts Classification Using a Hierarchical Mixture Model. Neural Comput. 14(9): 2221-2244 (2002) - [c3]Christopher K. I. Williams, Michalis K. Titsias:
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. NIPS 2002: 1391-1398 - [c2]Constantinos Constantinopoulos, Michalis K. Titsias, Aristidis Likas:
A Bayesian Regularization Method for the Probabilistic RBF Network. SETN 2002: 337-345 - 2001
- [j1]Michalis K. Titsias, Aristidis Likas:
Shared kernel models for class conditional density estimation. IEEE Trans. Neural Networks 12(5): 987-997 (2001) - 2000
- [c1]Michalis K. Titsias, Aristidis Likas:
A Probabilistic RBF Network for Classification. IJCNN (4) 2000: 238-243
Coauthor Index
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