Browse by UCL people
Group by: Type | Date
Number of items: 30.
Article
Alexopoulos, Angelos;
Dellaportas, Petros;
Titsias, Michalis K;
(2022)
Variance Reduction for Metropolis-Hastings Samplers.
Statistics and Computing
, 33
, Article 6. 10.1007/s11222-022-10183-2.
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Alexopoulos, A;
Dellaportas, P;
Forster, JJ;
(2019)
Bayesian forecasting of mortality rates by using latent Gaussian models.
Journal of the Royal Statistical Society: Series A (Statistics in Society)
, 182
(2)
pp. 689-711.
10.1111/rssa.12422.
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Arakelian, V;
Dellaportas, P;
Savona, R;
Vezzoli, M;
(2019)
Sovereign risk zones in Europe during and after the debt crisis.
Quantitative Finance
, 19
(6)
pp. 961-980.
10.1080/14697688.2018.1562197.
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Ballarin, G;
Dellaportas, P;
Grigoryeva, L;
Hirt, M;
van Huellen, S;
Ortega, JP;
(2023)
Reservoir computing for macroeconomic forecasting with mixed-frequency data.
International Journal of Forecasting
10.1016/j.ijforecast.2023.10.009.
(In press).
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Dellaportas, P;
Alexopoulos, A;
Papaspiliopoulos, O;
(2022)
Bayesian prediction of jumps in large panels of time series data.
Bayesian Analysis
10.1214/21-BA1268.
(In press).
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Dellaportas, P;
Ioannidis, E;
Kotsogiannis, C;
(2021)
Sample size determination for risk-based tax auditing.
Journal of the Royal Statistical Society Series A: Statistics in Society
, 184
(2)
pp. 479-493.
10.1111/rssa.12618.
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Dellaportas, P;
Titsias, M;
Petrova, K;
Plataniotis, A;
(2023)
Scalable inference for a full multivariate stochastic volatility model.
Journal of Econometrics
, 232
(2)
pp. 501-520.
10.1016/j.jeconom.2021.09.013.
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Dellaportas, P;
Stephens, DA;
(2020)
Interview with Professor Adrian FM Smith.
International Statistical Review
10.1111/insr.12395.
(In press).
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Dellaportas, P;
Tsionas, MG;
(2019)
Importance sampling from posterior distributions using copula-base approximations.
Journal of Econometrics
, 210
(1)
pp. 45-57.
10.1016/j.jeconom.2018.11.004.
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Finke, A;
King, R;
Beskos, A;
Dellaportas, P;
(2019)
Efficient Sequential Monte Carlo Algorithms for Integrated Population Models.
Journal of Agricultural, Biological and Environmental Statistics
, 24
(2)
pp. 204-224.
10.1007/s13253-018-00349-9.
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Fiocchi, Filippo;
Ladopoulou, Domna;
Dellaportas, Petros;
(2025)
Probabilistic Multilayer Perceptrons for Wind Farm Condition Monitoring.
Wind Energy
, 28
(4)
, Article e70012. 10.1002/we.70012.
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Hirt, Marcel;
Kreouzis, Vasileios;
Dellaportas, Petros;
(2024)
Learning variational autoencoders via MCMC speed measures.
Statistics and Computing
, 34
(5)
, Article 164. 10.1007/s11222-024-10481-x.
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Narayanan, Santhosh;
Kosmidis, Ioannis;
Dellaportas, Petros;
(2022)
Flexible marked spatio-temporal point processes with applications to event sequences from association football.
Journal of the Royal Statistical Society Series C: Applied Statistics
, 72
(5)
pp. 1095-1126.
10.1093/jrsssc/qlad085.
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Panos, A;
Dellaportas, P;
Titsias, M;
(2021)
Large Scale Multi-Label Learning using Gaussian Processes.
Machine Learning
10.1007/s10994-021-05952-5.
(In press).
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Rackham, OJL;
Langley, SR;
Oates, T;
Vradi, E;
Harmston, N;
Srivastava, PK;
Behmoaras, J;
... Petretto, E; + view all
(2017)
A Bayesian Approach for Analysis of Whole-Genome Bisulfite Sequencing Data Identifies Disease-Associated Changes in DNA Methylation.
Genetics
, 205
(4)
pp. 1443-1458.
10.1534/genetics.116.195008.
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Savona, Roberto;
Modena, Andrea;
Alessi, Lucia;
Alberini, Cristina Maria;
Baussano, Iacopo;
Guerra, Ranieri;
Pecorelli, Sergio;
... Stein, Roger M; + view all
(2025)
Towards a framework for a new research ecosystem.
Humanities and Social Sciences Communications
, 12
, Article 1044. 10.1057/s41599-025-05281-1.
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Stival, M;
Bernardi, M;
Cattelan, M;
Dellaportas, P;
(2023)
Missing data patterns in runners’ careers: do they matter?
Journal of the Royal Statistical Society. Series C: Applied Statistics
, 72
(1)
pp. 213-230.
10.1093/jrsssc/qlad009.
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Stival, M;
Bernardi, M;
Dellaportas, P;
(2023)
Doubly-online changepoint detection for monitoring health status during sports activities.
Annals of Applied Statistics
, 17
(3)
pp. 2387-2409.
10.1214/22-AOAS1724.
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Wang, Zhongzhen;
Dellaportas, Petros;
Kosmidis, Ioannis;
(2023)
Bayesian tensor factorisations for time series of counts.
Machine Learning
10.1007/s10994-023-06441-7.
(In press).
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Proceedings paper
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Daskalakis, C;
Dellaportas, P;
Panos, A;
(2022)
How Good are Low-Rank Approximations in Gaussian Process Regression?
In:
Proceedings of 36th AAAI conference on Artificial intelligence.
(pp. pp. 6463-6470).
AAAI (Association for the Advancement of Artificial Intelligence): Palo Alto, CA, USA.
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Dellaportas, P;
Titsias, M;
(2019)
Gradient-based Adaptive Markov Chain Monte Carlo.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
33rd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada.
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Hirt, M;
Titsias, MK;
Dellaportas, P;
(2021)
Entropy-based adaptive Hamiltonian Monte Carlo.
In:
Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021).
NeurIPS: Online.
(In press).
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Hirt, M;
Dellaportas, P;
(2019)
Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers.
In: Chaudhuri, K and Sugiyama, M, (eds.)
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics.
Proceedings of Machine Learning Research (PMLR): Okinawa, Japan.
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Hirt, M;
Dellaportas, P;
Durmus, A;
(2019)
Copula-like variational inference.
In:
Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019).
33rd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada.
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Panos, A;
Kosmidis, I;
Dellaportas, P;
(2023)
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences.
In:
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics.
(pp. pp. 236-252).
Proceedings of Machine Learning Research (PMLR): Valencia, Spain.
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Sellier, J;
Dellaportas, P;
(2023)
Sparse Spectral Bayesian Permanental Process with Generalized Kernel.
In:
Proceedings of Machine Learning Research (PMLR).
(pp. pp. 2769-2791).
MLResearchPress
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Sellier, J;
Dellaportas, P;
(2023)
Bayesian online change point detection with Hilbert space approximate Student-t process.
In:
Proceedings of the 40th International Conference on Machine Learning.
(pp. pp. 30553-30569).
PMLR (Proceedings of Machine Learning Research)
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Working / discussion paper
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Dellaportas, P;
Mijatović, A;
(2014)
Arbitrage-free prediction of the implied volatility smile.
ArXiv: Ithaca, NY, USA.
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Narayanan, Santhosh;
Kosmidis, Ioannis;
Dellaportas, Petros;
(2021)
Flexible marked spatio-temporal point processes with applications to event sequences from association football.
arXiv.org: Ithaca (NY), USA.
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Stival, Mattia;
Bernardi, Mauro;
Cattelan, Manuela;
Dellaportas, Petros;
(2022)
Missing data patterns in runners' careers: do they matter?
ArXiv: Ithaca, NY, USA.
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