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Petros Dellaportas
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2020 – today
- 2024
- [j12]Zhongzhen Wang, Petros Dellaportas
, Ioannis Kosmidis:
Bayesian tensor factorisations for time series of counts. Mach. Learn. 113(6): 3731-3750 (2024) - [j11]Marcel Hirt, Vasileios Kreouzis, Petros Dellaportas:
Learning variational autoencoders via MCMC speed measures. Stat. Comput. 34(5): 164 (2024) - [i9]Filippo Fiocchi, Domna Ladopoulou, Petros Dellaportas:
Probabilistic Multi-Layer Perceptrons for Wind Farm Condition Monitoring. CoRR abs/2404.16496 (2024) - [i8]Siran Liu, Petros Dellaportas, Michalis K. Titsias:
Can independent Metropolis beat crude Monte Carlo? CoRR abs/2406.17699 (2024) - 2023
- [j10]Angelos Alexopoulos
Variance reduction for Metropolis-Hastings samplers. Stat. Comput. 33(1): 6 (2023) - [c8]Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas:
Scalable marked point processes for exchangeable and non-exchangeable event sequences. AISTATS 2023: 236-252 - [c7]Jeremy Sellier, Petros Dellaportas:
Sparse Spectral Bayesian Permanental Process with Generalized Kernel. AISTATS 2023: 2769-2791 - [c6]Jeremy Sellier, Petros Dellaportas:
Bayesian online change point detection with Hilbert space approximate Student-t process. ICML 2023: 30553-30569 - [i7]Marcel Hirt, Vasileios Kreouzis, Petros Dellaportas:
Learning variational autoencoders via MCMC speed measures. CoRR abs/2308.13731 (2023) - 2022
- [c5]Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos:
How Good Are Low-Rank Approximations in Gaussian Process Regression? AAAI 2022: 6463-6470 - 2021
- [j9]Aristeidis Panos
Large scale multi-label learning using Gaussian processes. Mach. Learn. 110(5): 965-987 (2021) - [c4]Marcel Hirt, Michalis K. Titsias, Petros Dellaportas:
Entropy-based adaptive Hamiltonian Monte Carlo. NeurIPS 2021: 28482-28495 - [i6]Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas:
Scalable and Interpretable Marked Point Processes. CoRR abs/2105.14574 (2021) - 2020
- [i5]Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos:
Faster Gaussian Processes via Deep Embeddings. CoRR abs/2004.01584 (2020)
2010 – 2019
- 2019
- [c3]Marcel Hirt, Petros Dellaportas:
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. AISTATS 2019: 76-86 - [c2]Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. NeurIPS 2019: 2955-2967 - [c1]Michalis K. Titsias, Petros Dellaportas:
Gradient-based Adaptive Markov Chain Monte Carlo. NeurIPS 2019: 15704-15713 - [i4]Marcel Hirt, Petros Dellaportas, Alain Durmus:
Copula-like Variational Inference. CoRR abs/1904.07153 (2019) - [i3]Michalis K. Titsias, Petros Dellaportas:
Gradient-based Adaptive Markov Chain Monte Carlo. CoRR abs/1911.01373 (2019) - 2018
- [i2]Marcel Hirt, Petros Dellaportas:
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers. CoRR abs/1805.09406 (2018) - [i1]Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias:
Fully Scalable Gaussian Processes using Subspace Inducing Inputs. CoRR abs/1807.02537 (2018) - 2015
- [j8]Owen J. L. Rackham
WGBSSuite: simulating whole-genome bisulphite sequencing data and benchmarking differential DNA methylation analysis tools. Bioinform. 31(14): 2371-2373 (2015) - 2012
- [j7]Petros Dellaportas
Cholesky-GARCH models with applications to finance. Stat. Comput. 22(4): 849-855 (2012) - 2011
- [j6]Loukia Meligkotsidou, Petros Dellaportas
Forecasting with non-homogeneous hidden Markov models. Stat. Comput. 21(3): 439-449 (2011)
2000 – 2009
- 2008
- [j5]D. Giannikis, Ioannis D. Vrontos, Petros Dellaportas
Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models. Comput. Stat. Data Anal. 52(3): 1549-1571 (2008) - 2006
- [j4]Petros Dellaportas
Multivariate mixtures of normals with unknown number of components. Stat. Comput. 16(1): 57-68 (2006) - 2005
- [j3]Stefanos G. Giakoumatos, Petros Dellaportas
Bayesian analysis of the unobserved ARCH model. Stat. Comput. 15(2): 103-111 (2005) - 2002
- [j2]Petros Dellaportas
On Bayesian model and variable selection using MCMC. Stat. Comput. 12(1): 27-36 (2002)
1990 – 1999
- 1996
- [j1]Ronald Cools
The role of embedded integration rules in Bayesian statistics. Stat. Comput. 6(3): 245-250 (1996)
Coauthor Index
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