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ORCIDÖmer Deniz Akyildiz
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2020 – today
- 2024
[j12]Alex Glyn-Davies
, Connor Duffin, Ömer Deniz Akyildiz, Mark Girolami:
Φ-DVAE: Physics-informed dynamical variational autoencoders for unstructured data assimilation. J. Comput. Phys. 515: 113293 (2024)- [j11]Ömer Deniz Akyildiz, Sotirios Sabanis:
Nonasymptotic analysis of Stochastic Gradient Hamiltonian Monte Carlo under local conditions for nonconvex optimization. J. Mach. Learn. Res. 25: 113:1-113:34 (2024) - [j10]Ömer Deniz Akyildiz, Dan Crisan, Joaquín Míguez:
Sequential Discretization Schemes for a Class of Stochastic Differential Equations and their Application to Bayesian Filtering. SIAM J. Numer. Anal. 62(2): 946-973 (2024) - [i11]Ömer Deniz Akyildiz, Mark Girolami, Andrew M. Stuart, Arnaud Vadeboncoeur:
Efficient Prior Calibration From Indirect Data. CoRR abs/2405.17955 (2024) - [i10]Alex Glyn-Davies, Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami:
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling. CoRR abs/2409.06560 (2024) - 2023
- [j9]Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak:
Fully probabilistic deep models for forward and inverse problems in parametric PDEs. J. Comput. Phys. 491: 112369 (2023) - [j8]Víctor Elvira, Emilie Chouzenoux, Ömer Deniz Akyildiz, Luca Martino:
Gradient-based adaptive importance samplers. J. Frankl. Inst. 360(13): 9490-9514 (2023) - [c8]Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz:
Random Grid Neural Processes for Parametric Partial Differential Equations. ICML 2023: 34759-34778 - [i9]Arnaud Vadeboncoeur, Ieva Kazlauskaite, Yanni Papandreou, Fehmi Cirak, Mark Girolami, Ömer Deniz Akyildiz:
Random Grid Neural Processes for Parametric Partial Differential Equations. CoRR abs/2301.11040 (2023) - [i8]Stefano Bruno, Ying Zhang, Dong-Young Lim, Ömer Deniz Akyildiz, Sotirios Sabanis:
On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates. CoRR abs/2311.13584 (2023) - 2022
- [j7]Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami:
Statistical Finite Elements via Langevin Dynamics. SIAM/ASA J. Uncertain. Quantification 10(4): 1560-1585 (2022) - [i7]Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite, Mark Girolami, Fehmi Cirak:
Deep Probabilistic Models for Forward and Inverse Problems in Parametric PDEs. CoRR abs/2208.04856 (2022) - [i6]Alex Glyn-Davies, Connor Duffin, Ömer Deniz Akyildiz, Mark Girolami:
$Φ$-DVAE: Learning Physically Interpretable Representations with Nonlinear Filtering. CoRR abs/2209.15609 (2022) - 2021
- [j6]Ömer Deniz Akyildiz, Joaquín Míguez:
Convergence rates for optimised adaptive importance samplers. Stat. Comput. 31(2): 12 (2021) - [c7]Ömer Deniz Akyildiz, Gerrit J. J. van den Burg, Theodoros Damoulas, Mark F. J. Steel:
Probabilistic Sequential Matrix Factorization. AISTATS 2021: 3484-3492 - [i5]Ömer Deniz Akyildiz, Connor Duffin, Sotirios Sabanis, Mark Girolami:
Statistical Finite Elements via Langevin Dynamics. CoRR abs/2110.11131 (2021) - 2020
- [j5]Ömer Deniz Akyildiz, Joaquín Míguez:
Nudging the particle filter. Stat. Comput. 30(2): 305-330 (2020) - [j4]Ömer Deniz Akyildiz, Dan Crisan, Joaquín Míguez:
Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization. Stat. Comput. 30(6): 1645-1663 (2020) - [c6]Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen:
Generalised Bayesian Filtering via Sequential Monte Carlo. NeurIPS 2020 - [c5]Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz:
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. NeurIPS 2020 - [i4]Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen:
Generalized Bayesian Filtering via Sequential Monte Carlo. CoRR abs/2002.09998 (2020) - [i3]Lorenz Richter, Ayman Boustati, Nikolas Nüsken, Francisco J. R. Ruiz, Ömer Deniz Akyildiz:
VarGrad: A Low-Variance Gradient Estimator for Variational Inference. CoRR abs/2010.10436 (2020)
2010 – 2019
- 2019
- [j3]Ömer Deniz Akyildiz, Joaquín Míguez:
Dictionary filtering: a probabilistic approach to online matrix factorisation. Signal Image Video Process. 13(4): 737-744 (2019) - [j2]Ömer Deniz Akyildiz, Emilie Chouzenoux, Víctor Elvira, Joaquín Míguez:
A Probabilistic Incremental Proximal Gradient Method. IEEE Signal Process. Lett. 26(8): 1257-1261 (2019) - [i2]Ying Zhang, Ömer Deniz Akyildiz, Theo Damoulas, Sotirios Sabanis:
Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under local conditions in nonconvex optimization. CoRR abs/1910.02008 (2019) - [i1]Ömer Deniz Akyildiz, Theodoros Damoulas, Mark F. J. Steel:
Probabilistic sequential matrix factorization. CoRR abs/1910.03906 (2019) - 2018
- [c4]Ömer Deniz Akyildiz, Victor Elvira, Joaquín Míguez:
The Incremental Proximal Method: A Probabilistic Perspective. ICASSP 2018: 4279-4283 - 2017
- [c3]Ömer Deniz Akyildiz, Inés P. Mariño, Joaquín Míguez:
Adaptive noisy importance sampling for stochastic optimization. CAMSAP 2017: 1-5 - 2014
- [j1]Ilker Bayram, Ömer Deniz Akyildiz:
Primal-dual algorithms for audio decomposition using mixed norms. Signal Image Video Process. 8(1): 95-110 (2014) - 2012
- [c2]Ömer Deniz Akyildiz, Ilker Bayram:
An analysis prior based decomposition method for audio signals. EUSIPCO 2012: 2437-2441 - [c1]Ömer Deniz Akyildiz, Ilker Bayram:
Signal decomposition via mixed-norm regularization. SIU 2012: 1-4
Coauthor Index
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