Dino Sejdinovic

ORCID

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 101

Top Venues

Publications

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 (2024)
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. AAAI (2024)
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. CoRR (2024)
Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin Bonilla
Bayesian Adaptive Calibration and Optimal Design. CoRR (2024)
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
Neural-Kernel Conditional Mean Embeddings. CoRR (2024)
Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Trans. Mach. Learn. Res. 2024 (2024)
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 (2023)
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS (2023)
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)
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Squared Neural Families: A New Class of Tractable Density Models. NeurIPS (2023)
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR (2023)
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. CoRR (2023)
Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. NeurIPS (2023)
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. ICML (2023)
Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. CoRR (2023)
Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
Squared Neural Families: A New Class of Tractable Density Models. CoRR (2023)
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
Explaining Preferences with Shapley Values. NeurIPS (2022)
Veit D. Wild, Robert Hu, Dino Sejdinovic
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. NeurIPS (2022)
Jake Fawkes, Robin Evans, Dino Sejdinovic
Selection, Ignorability and Challenges With Causal Fairness. CLeaR (2022)
Qinyi Zhang, Veit Wild, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic
Bayesian Kernel Two-Sample Testing. J. Comput. Graph. Stat. 31 (4) (2022)
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
Explaining Preferences with Shapley Values. CoRR (2022)
Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
Spectral Ranking with Covariates. ECML/PKDD (5) (2022)
Siu Lun Chau, Javier González, Dino Sejdinovic
Learning Inconsistent Preferences with Gaussian Processes. AISTATS (2022)
Robert Hu, Dino Sejdinovic, Joan Alexis Glaunès
Giga-scale Kernel Matrix Vector Multiplication on GPU. CoRR (2022)
Jake Fawkes, Robin Evans, Dino Sejdinovic
Selection, Ignorability and Challenges With Causal Fairness. CoRR (2022)
David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic
Survival regression with proper scoring rules and monotonic neural networks. AISTATS (2022)
Diego Martinez-Taboada, Dino Sejdinovic
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. CoRR (2022)
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 (2022)
Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton
Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR (2022)
Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. CoRR (2022)
Veit D. Wild, Robert Hu, Dino Sejdinovic
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. CoRR (2022)
Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic
RKHS-SHAP: Shapley Values for Kernel Methods. NeurIPS (2022)
Diego Martinez-Taboada, Dino Sejdinovic
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. CoRR (2022)
Zhu Li, Adrián Pérez-Suay, Gustau Camps-Valls, Dino Sejdinovic
Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. Pattern Recognit. 132 (2022)
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
Large scale tensor regression using kernels and variational inference. Mach. Learn. 111 (7) (2022)
Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès
Giga-scale Kernel Matrix-Vector Multiplication on GPU. NeurIPS (2022)
Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. AISTATS (2021)
Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Deconditional Downscaling with Gaussian Processes. CoRR (2021)
Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
Variational inference with continuously-indexed normalizing flows. UAI (2021)
B. Severin, Dominic T. Lennon, Leon C. Camenzind, Florian Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M. J. Carballido, S. 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 (2021)
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. CoRR (2021)
David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic
Time-to-event regression using partially monotonic neural networks. CoRR (2021)
Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
Deconditional Downscaling with Gaussian Processes. NeurIPS (2021)
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 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) (2021)
Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth Flaxman
Unrepresentative big surveys significantly overestimated US vaccine uptake. Nat. 600 (7890) (2021)
Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu
Meta Learning for Causal Direction. AAAI (2021)
Siu Lun Chau, Javier Gonzalez, Dino Sejdinovic
RKHS-SHAP: Shapley Values for Kernel Methods. CoRR (2021)
David L. Craig, H. Moon, Federico Fedele, Dominic T. Lennon, B. 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 (2021)
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
Towards a Unified Analysis of Random Fourier Features. J. Mach. Learn. Res. 22 (2021)
Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS (2021)
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 (2021)
Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR (2021)
Robert Hu, Dino Sejdinovic
Robust Deep Interpretable Features for Binary Image Classification. NLDL (2021)
Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
Spectral Ranking with Covariates. CoRR (2020)
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
Large Scale Tensor Regression using Kernels and Variational Inference. CoRR (2020)
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein
A Perspective on Gaussian Processes for Earth Observation. CoRR (2020)
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Deep Gaussian Processes. CoRR (2020)
Siu Lun Chau, Javier González, Dino Sejdinovic
Learning Inconsistent Preferences with Kernel Methods. CoRR (2020)
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 (2020)
Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR (2020)
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 (2020)
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 (2020)
Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. CoRR (2020)
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
Meta Learning for Causal Direction. CoRR (2020)
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
Inter-domain Deep Gaussian Processes. ICML (2020)
Zhu Li, Weijie Su, Dino Sejdinovic
Benign Overfitting and Noisy Features. CoRR (2020)
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls, Dino Sejdinovic
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. CoRR (2019)
Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic
Hyperparameter Learning via Distributional Transfer. NeurIPS (2019)
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park
A Differentially Private Kernel Two-Sample Test. ECML/PKDD (1) (2019)
Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. CoRR (2019)
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
Towards a Unified Analysis of Random Fourier Features. ICML (2019)
Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier
Detecting anthropogenic cloud perturbations with deep learning. CoRR (2019)
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
A Unified Analysis of Random Fourier Features. CoRR (2018)
François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR (2018)
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 (2018)
Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic
Large-scale kernel methods for independence testing. Stat. Comput. 28 (1) (2018)
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Hamiltonian Variational Auto-Encoder. CoRR (2018)
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Causal Inference via Kernel Deviance Measures. NeurIPS (2018)
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR (2018)
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park
A Differentially Private Kernel Two-Sample Test. CoRR (2018)
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Hamiltonian Variational Auto-Encoder. NeurIPS (2018)
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
Bayesian Approaches to Distribution Regression. AISTATS (2018)
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)
Ho Chung Leon Law, Peilin Zhao, Junzhou Huang, Dino Sejdinovic
Hyperparameter Learning via Distributional Transfer. CoRR (2018)
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Causal Inference via Kernel Deviance Measures. CoRR (2018)
Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic
Poisson intensity estimation with reproducing kernels. AISTATS (2017)
Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic
Testing and Learning on Distributions with Symmetric Noise Invariance. NIPS (2017)
Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
Bayesian Distribution Regression. CoRR (2017)
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Deep Kernel Machines via the Kernel Reparametrization Trick. ICLR (Workshop) (2017)
Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic
Feature-to-Feature Regression for a Two-Step Conditional Independence Test. UAI (2017)
Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
Kernel Sequential Monte Carlo. ECML/PKDD (1) (2017)
Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. AISTATS (2016)
Gianni Franchi, Jesús Angulo, Dino Sejdinovic
Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. CoRR (2016)
Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
Bayesian Learning of Kernel Embeddings. UAI (2016)
Brooks Paige, Dino Sejdinovic, Frank D. Wood
Super-Sampling with a Reservoir. UAI (2016)
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 (2016)
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. ICML (2016)
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 (2016)
Gianni Franchi, Jesús Angulo, Dino Sejdinovic
Hyperspectral image classification with support vector machines on kernel distribution embeddings. ICIP (2016)
Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic
Network function computation as a service in future 5G machine type communications. ISTC (2016)

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Publication Activity (10 Years)

Top Topics

Top Venues

Dino Sejdinovic