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ORCIDDino Sejdinovic
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2020 – today
- 2024
[j17]Robert Hu, Dino Sejdinovic, Robin J. Evans:
A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. J. Mach. Learn. Res. 25: 160:1-160:56 (2024)- [j16]Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic:
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Trans. Mach. Learn. Res. 2024 (2024) - [c59]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. AAAI 2024: 20559-20566 - [c58]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. ICML 2024 - [i64]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. CoRR abs/2402.09608 (2024) - [i63]Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic:
Neural-Kernel Conditional Mean Embeddings. CoRR abs/2403.10859 (2024) - [i62]Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla:
Bayesian Adaptive Calibration and Optimal Design. CoRR abs/2405.14440 (2024) - [i61]Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damith C. Ranasinghe, Ehsan Abbasnejad:
Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks. CoRR abs/2407.20891 (2024) - [i60]Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet:
Credal Two-Sample Tests of Epistemic Ignorance. CoRR abs/2410.12921 (2024) - [i59]Dino Sejdinovic:
An Overview of Causal Inference using Kernel Embeddings. CoRR abs/2410.22754 (2024) - 2023
- [j15]Jonas Schuff
, Dominic T. Lennon, Simon Geyer, David L. Craig, Federico Fedele, Florian Vigneau, Leon C. Camenzind, Andreas V. Kuhlmann, G. Andrew D. Briggs, Dominik M. Zumbühl, Dino Sejdinovic, Natalia Ares:
Identifying Pauli spin blockade using deep learning. Quantum 7: 1077 (2023) - [j14]Adrian Perez-Suay, Paula Gordaliza, Jean-Michel Loubes, Dino Sejdinovic, Gustau Camps-Valls:
Fair Kernel Regression through Cross-Covariance Operators. Trans. Mach. Learn. Res. 2023 (2023) - [c57]Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. ICML 2023: 2885-2913 - [c56]Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic:
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS 2023 - [c55]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Squared Neural Families: A New Class of Tractable Density Models. NeurIPS 2023 - [c54]Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch:
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. NeurIPS 2023 - [i58]Shahine Bouabid, Jake Fawkes, Dino Sejdinovic:
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. CoRR abs/2301.11214 (2023) - [i57]Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic:
Squared Neural Families: A New Class of Tractable Density Models. CoRR abs/2305.13552 (2023) - [i56]Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch:
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. CoRR abs/2305.15027 (2023) - [i55]Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic:
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR abs/2305.15167 (2023) - 2022
- [j13]Qinyi Zhang, Veit Wild, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic:
Bayesian Kernel Two-Sample Testing. J. Comput. Graph. Stat. 31(4): 1164-1176 (2022) - [j12]Robert Hu, Geoff K. Nicholls, Dino Sejdinovic:
Large scale tensor regression using kernels and variational inference. Mach. Learn. 111(7): 2663-2713 (2022) - [j11]Zhu Li, Adrián Pérez-Suay, Gustau Camps-Valls, Dino Sejdinovic:
Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. Pattern Recognit. 132: 108922 (2022) - [c53]David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic:
Survival regression with proper scoring rules and monotonic neural networks. AISTATS 2022: 1190-1205 - [c52]Siu Lun Chau, Javier González, Dino Sejdinovic:
Learning Inconsistent Preferences with Gaussian Processes. AISTATS 2022: 2266-2281 - [c51]Jake Fawkes, Robin J. Evans, Dino Sejdinovic:
Selection, Ignorability and Challenges With Causal Fairness. CLeaR 2022: 275-289 - [c50]Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic:
RKHS-SHAP: Shapley Values for Kernel Methods. NeurIPS 2022 - [c49]Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic:
Explaining Preferences with Shapley Values. NeurIPS 2022 - [c48]Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès:
Giga-scale Kernel Matrix-Vector Multiplication on GPU. NeurIPS 2022 - [c47]Veit D. Wild, Robert Hu, Dino Sejdinovic:
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. NeurIPS 2022 - [c46]Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic:
Spectral Ranking with Covariates. ECML/PKDD (5) 2022: 70-86 - [i54]Jonas Schuff, Dominic T. Lennon, Simon Geyer, David L. Craig, Federico Fedele, Florian Vigneau, Leon C. Camenzind, Andreas V. Kuhlmann, G. Andrew D. Briggs, Dominik M. Zumbühl, Dino Sejdinovic, Natalia Ares:
Identifying Pauli spin blockade using deep learning. CoRR abs/2202.00574 (2022) - [i53]Robert Hu, Dino Sejdinovic, Joan Alexis Glaunès:
Giga-scale Kernel Matrix Vector Multiplication on GPU. CoRR abs/2202.01085 (2022) - [i52]Jake Fawkes, Robin J. Evans, Dino Sejdinovic:
Selection, Ignorability and Challenges With Causal Fairness. CoRR abs/2202.13774 (2022) - [i51]Veit D. Wild, Robert Hu, Dino Sejdinovic:
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. CoRR abs/2205.06342 (2022) - [i50]Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic:
Explaining Preferences with Shapley Values. CoRR abs/2205.13662 (2022) - [i49]Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton:
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR abs/2206.11142 (2022) - [i48]Diego Martinez-Taboada, Dino Sejdinovic:
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. CoRR abs/2210.13692 (2022) - [i47]Diego Martinez-Taboada, Dino Sejdinovic:
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. CoRR abs/2211.01518 (2022) - [i46]Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic:
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. CoRR abs/2212.04922 (2022) - 2021
- [j10]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
Towards a Unified Analysis of Random Fourier Features. J. Mach. Learn. Res. 22: 108:1-108:51 (2021) - [j9]Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth R. Flaxman:
Unrepresentative big surveys significantly overestimated US vaccine uptake. Nat. 600(7890): 695-700 (2021) - [j8]Gordon S. Blair, Richard Bassett, Lucy Bastin, Lindsay Beevers, Maribel Isabel Borrajo, Mike Brown, Sarah L. Dance, Ada Dionescu, Liz Edwards, Maria Angela Ferrario, Rob Fraser, Harriet Fraser, Simon Gardner, Peter A. Henrys, Tony Hey, Stuart Homann, Chantal Huijbers, James Hutchison, Phil Jonathan, Rob Lamb, Sophie Laurie, Amber Leeson, David Leslie, Malcolm McMillan, Vatsala Nundloll, Oluwole K. Oyebamiji, Jordan Phillipson, Vicky Pope, Rachel Prudden, Stefan Reis, Maria Salama, Faiza Samreen, Dino Sejdinovic, Will Simm, Roger Street, Lauren Thornton, Ross Towe, Joshua Vande Hey, Massimo Vieno, Joanne A. Waller, John Watkins:
The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. Patterns 2(1): 100156 (2021) - [j7]Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic:
Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. IEEE Trans. Signal Inf. Process. over Networks 7: 192-207 (2021) - [c45]Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. AAAI 2021: 9897-9905 - [c44]Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic:
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. AISTATS 2021: 1099-1107 - [c43]Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic:
BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS 2021: 3466-3477 - [c42]Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic:
Deconditional Downscaling with Gaussian Processes. NeurIPS 2021: 17813-17825 - [c41]Robert Hu, Dino Sejdinovic:
Robust Deep Interpretable Features for Binary Image Classification. NLDL 2021 - [c40]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational inference with continuously-indexed normalizing flows. UAI 2021: 44-53 - [i45]David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic:
Time-to-event regression using partially monotonic neural networks. CoRR abs/2103.14755 (2021) - [i44]Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic:
Deconditional Downscaling with Gaussian Processes. CoRR abs/2105.12909 (2021) - [i43]Veit Wild, Motonobu Kanagawa, Dino Sejdinovic:
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. CoRR abs/2106.01121 (2021) - [i42]Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic:
BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR abs/2106.03477 (2021) - [i41]Brandon Severin, Dominic T. Lennon, Leon C. Camenzind, Florian Vigneau, Federico Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, Miguel J. Carballido, Simon Svab, Andreas V. Kuhlmann, F. R. Braakman, Simon Geyer, F. N. M. Froning, H. Moon, Michael A. Osborne, Dino Sejdinovic, G. Katsaros, Dominik M. Zumbühl, G. Andrew D. Briggs, Natalia Ares:
Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. CoRR abs/2107.12975 (2021) - [i40]Siu Lun Chau, Javier González, Dino Sejdinovic:
RKHS-SHAP: Shapley Values for Kernel Methods. CoRR abs/2110.09167 (2021) - [i39]David L. Craig, H. Moon, Federico Fedele, Dominic T. Lennon, Barnaby van Straaten, Florian Vigneau, Leon C. Camenzind, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares:
Bridging the reality gap in quantum devices with physics-aware machine learning. CoRR abs/2111.11285 (2021) - 2020
- [c39]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. ICML 2020: 8286-8294 - [i38]Hyungil Moon, Dominic T. Lennon, James Kirkpatrick, Nina M. van Esbroeck, Leon C. Camenzind, Liuqi Yu, Florian Vigneau, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Edward A. Laird, Natalia Ares:
Machine learning enables completely automatic tuning of a quantum device faster than human experts. CoRR abs/2001.02589 (2020) - [i37]Nina M. van Esbroeck, Dominic T. Lennon, Hyungil Moon, V. Nguyen, Florian Vigneau, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Dino Sejdinovic, Natalia Ares:
Quantum device fine-tuning using unsupervised embedding learning. CoRR abs/2001.04409 (2020) - [i36]Robert Hu, Geoff K. Nicholls, Dino Sejdinovic:
Large Scale Tensor Regression using Kernels and Variational Inference. CoRR abs/2002.04704 (2020) - [i35]Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic:
Spectral Ranking with Covariates. CoRR abs/2005.04035 (2020) - [i34]Siu Lun Chau, Javier González, Dino Sejdinovic:
Learning Inconsistent Preferences with Kernel Methods. CoRR abs/2006.03847 (2020) - [i33]Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein:
A Perspective on Gaussian Processes for Earth Observation. CoRR abs/2007.01238 (2020) - [i32]Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu:
Meta Learning for Causal Direction. CoRR abs/2007.02809 (2020) - [i31]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR abs/2007.05426 (2020) - [i30]Zhu Li, Weijie J. Su, Dino Sejdinovic:
Benign Overfitting and Noisy Features. CoRR abs/2008.02901 (2020) - [i29]Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic:
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. CoRR abs/2008.10065 (2020) - [i28]V. Nguyen, S. B. Orbell, Dominic T. Lennon, Hyungil Moon, Florian Vigneau, Leon C. Camenzind, Liuqi Yu, Dominik M. Zumbühl, G. Andrew D. Briggs, Michael A. Osborne, Dino Sejdinovic, Natalia Ares:
Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. CoRR abs/2009.14825 (2020) - [i27]Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal:
Inter-domain Deep Gaussian Processes. CoRR abs/2011.00415 (2020)
2010 – 2019
- 2019
- [c38]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
Towards a Unified Analysis of Random Fourier Features. ICML 2019: 3905-3914 - [c37]Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic:
Hyperparameter Learning via Distributional Transfer. NeurIPS 2019: 6801-6812 - [c36]Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park:
A Differentially Private Kernel Two-Sample Test. ECML/PKDD (1) 2019: 697-724 - [i26]Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic:
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. CoRR abs/1906.02236 (2019) - [i25]Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls, Dino Sejdinovic:
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. CoRR abs/1911.04322 (2019) - [i24]Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier:
Detecting anthropogenic cloud perturbations with deep learning. CoRR abs/1911.13061 (2019) - 2018
- [j6]Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic:
Large-scale kernel methods for independence testing. Stat. Comput. 28(1): 113-130 (2018) - [c35]Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Approaches to Distribution Regression. AISTATS 2018: 1167-1176 - [c34]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. NeurIPS 2018: 6084-6094 - [c33]Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
Causal Inference via Kernel Deviance Measures. NeurIPS 2018: 6986-6994 - [c32]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. NeurIPS 2018: 8178-8188 - [i23]Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
Causal Inference via Kernel Deviance Measures. CoRR abs/1804.04622 (2018) - [i22]Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu:
Variational Learning on Aggregate Outputs with Gaussian Processes. CoRR abs/1805.08463 (2018) - [i21]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. CoRR abs/1805.11328 (2018) - [i20]Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic:
A Unified Analysis of Random Fourier Features. CoRR abs/1806.09178 (2018) - [i19]Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur:
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR abs/1807.02582 (2018) - [i18]Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park:
A Differentially Private Kernel Two-Sample Test. CoRR abs/1808.00380 (2018) - [i17]Ho Chung Leon Law, Peilin Zhao, Junzhou Huang, Dino Sejdinovic:
Hyperparameter Learning via Distributional Transfer. CoRR abs/1810.06305 (2018) - [i16]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR abs/1811.10275 (2018) - 2017
- [c31]Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic:
Poisson intensity estimation with reproducing kernels. AISTATS 2017: 270-279 - [c30]Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
Deep Kernel Machines via the Kernel Reparametrization Trick. ICLR (Workshop) 2017 - [c29]Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic:
Testing and Learning on Distributions with Symmetric Noise Invariance. NIPS 2017: 1343-1353 - [c28]Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic:
Kernel Sequential Monte Carlo. ECML/PKDD (1) 2017: 390-409 - [c27]Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic:
Feature-to-Feature Regression for a Two-Step Conditional Independence Test. UAI 2017 - [i15]Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman:
Bayesian Distribution Regression. CoRR abs/1705.04293 (2017) - 2016
- [j5]Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic, Günes Karabulut-Kurt, Camilla Hollanti, Ingo Fischer:
CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. IEEE Access 4: 3360-3378 (2016) - [c26]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. AISTATS 2016: 398-407 - [c25]Gianni Franchi, Jesús Angulo, Dino Sejdinovic:
Hyperspectral image classification with support vector machines on kernel distribution embeddings. ICIP 2016: 1898-1902 - [c24]Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. ICML 2016: 1482-1491 - [c23]Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic:
Network function computation as a service in future 5G machine type communications. ISTC 2016: 365-369 - [c22]Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi:
Bayesian Learning of Kernel Embeddings. UAI 2016 - [c21]Brooks Paige, Dino Sejdinovic, Frank D. Wood:
Super-Sampling with a Reservoir. UAI 2016 - [i14]Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh:
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. CoRR abs/1602.04805 (2016) - [i13]Gianni Franchi, Jesús Angulo, Dino Sejdinovic:
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. CoRR abs/1605.09136 (2016) - [i12]Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic, Gunes Karabulut-Kurt, Camilla Hollanti, Ingo Fischer:
CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. CoRR abs/1609.03363 (2016) - 2015
- [c20]Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton:
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS 2015: 955-963 - [c19]Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton:
Fast Two-Sample Testing with Analytic Representations of Probability Measures. NIPS 2015: 1981-1989 - [c18]Dejan Vukobratovic, Dino Sejdinovic, Aleksandra Pizurica:
Compressed Sensing using sparse binary measurements: A rateless coding perspective. SPAWC 2015: 86-90 - [c17]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI 2015: 405-414 - [i11]Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami:
Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR abs/1501.03326 (2015) - [i10]Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic:
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. CoRR abs/1502.02558 (2015) - [i9]Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó:
Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR abs/1503.02551 (2015) - [i8]François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic:
Probabilistic Integration. CoRR abs/1512.00933 (2015) - 2014
- [c16]Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. ICML 2014: 1665-1673 - [c15]Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton:
A Wild Bootstrap for Degenerate Kernel Tests. NIPS 2014: 3608-3616 - 2013
- [c14]Dino Sejdinovic, Arthur Gretton, Wicher Bergsma:
A Kernel Test for Three-Variable Interactions. NIPS 2013: 1124-1132 - [i7]Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. CoRR abs/1307.5302 (2013) - 2012
- [c13]Andreas Müller, Dino Sejdinovic, Robert J. Piechocki:
Approximate message passing under finite alphabet constraints. ICASSP 2012: 3177-3180 - [c12]