Petros Dellaportas

Publication Activity (10 Years)

Years Active: 1996-2024
Publications (10 Years): 22

Top Topics

Gaussian Process

Top Venues

Publications

Zhongzhen Wang, Petros Dellaportas, Ioannis Kosmidis
Bayesian tensor factorisations for time series of counts. Mach. Learn. 113 (6) (2024)
Marcel Hirt, Vasileios Kreouzis, Petros Dellaportas
Learning variational autoencoders via MCMC speed measures. Stat. Comput. 34 (5) (2024)
Siran Liu, Petros Dellaportas, Michalis K. Titsias
Can independent Metropolis beat crude Monte Carlo? CoRR (2024)
Filippo Fiocchi, Domna Ladopoulou, Petros Dellaportas
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring. CoRR (2024)
Angelos Alexopoulos, Petros Dellaportas, Michalis K. Titsias
Variance reduction for Metropolis-Hastings samplers. Stat. Comput. 33 (1) (2023)
Marcel Hirt, Vasileios Kreouzis, Petros Dellaportas
Learning variational autoencoders via MCMC speed measures. CoRR (2023)
Jeremy Sellier, Petros Dellaportas
Sparse Spectral Bayesian Permanental Process with Generalized Kernel. AISTATS (2023)
Jeremy Sellier, Petros Dellaportas
Bayesian online change point detection with Hilbert space approximate Student-t process. ICML (2023)
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
Scalable marked point processes for exchangeable and non-exchangeable event sequences. AISTATS (2023)
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
How Good Are Low-Rank Approximations in Gaussian Process Regression? AAAI (2022)
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
Scalable and Interpretable Marked Point Processes. CoRR (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)
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
How Good are Low-Rank Approximations in Gaussian Process Regression? CoRR (2021)
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
Faster Gaussian Processes via Deep Embeddings. CoRR (2020)
Marcel Hirt, Petros Dellaportas, Alain Durmus
Copula-like Variational Inference. CoRR (2019)
Michalis K. Titsias, Petros Dellaportas
Gradient-based Adaptive Markov Chain Monte Carlo. CoRR (2019)
Michalis K. Titsias, Petros Dellaportas
Gradient-based Adaptive Markov Chain Monte Carlo. NeurIPS (2019)
Marcel Hirt, Petros Dellaportas
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. AISTATS (2019)
Marcel Hirt, Petros Dellaportas, Alain Durmus
Copula-like Variational Inference. NeurIPS (2019)
Marcel Hirt, Petros Dellaportas
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. CoRR (2018)
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
Fully Scalable Gaussian Processes using Subspace Inducing Inputs. CoRR (2018)
Owen J. L. Rackham, Petros Dellaportas, Enrico Petretto, Leonardo Bottolo
WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools. Bioinform. 31 (14) (2015)
Petros Dellaportas, Mohsen Pourahmadi
Cholesky-GARCH models with applications to finance. Stat. Comput. 22 (4) (2012)
Loukia Meligkotsidou, Petros Dellaportas
Forecasting with non-homogeneous hidden Markov models. Stat. Comput. 21 (3) (2011)
D. Giannikis, I. D. Vrontos, Petros Dellaportas
Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models. Comput. Stat. Data Anal. 52 (3) (2008)
Petros Dellaportas, Ioulia Papageorgiou
Multivariate mixtures of normals with unknown number of components. Stat. Comput. 16 (1) (2006)
Stefanos G. Giakoumatos, Petros Dellaportas, Dimitris Nicolas Politis
Bayesian analysis of the unobserved ARCH model. Stat. Comput. 15 (2) (2005)
Petros Dellaportas, Jonathan J. Forster, Ioannis Ntzoufras
On Bayesian model and variable selection using MCMC. Stat. Comput. 12 (1) (2002)
Ronald Cools, Petros Dellaportas
The role of embedded integration rules in Bayesian statistics. Stat. Comput. 6 (3) (1996)