Dino Sejdinovic

155 papers A* 24A 14B 4Misc 1Journal 91Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Instrumental and Proximal Causal Inference with Gaussian Processes. CoRR Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic, Edwin Fong, Siu Lun Chau
2026	J	`jnl`	Observationally Informed Adaptive Causal Experimental Design. CoRR Erdun Gao, Liang Zhang, Jake Fawkes, Aoqi Zuo, Wenqin Liu, Haoxuan Li, Mingming Gong, Dino Sejdinovic
2025	J	`jnl`	ActiveCQ: Active Estimation of Causal Quantities. CoRR Erdun Gao, Dino Sejdinovic
2025	—	`conf`	All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. AI (1) Peter Moskvichev, Dino Sejdinovic
2025	J	`jnl`	All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. CoRR Peter Moskvichev, Dino Sejdinovic
2025	A*	`conf`	Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks. AAAI Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damien Teney, Damith C. Ranasinghe, Ehsan Abbasnejad
2025	J	`jnl`	Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation. CoRR Erdun Gao, Jake Fawkes, Dino Sejdinovic
2025	A	`conf`	Credal Two-Sample Tests of Epistemic Uncertainty. AISTATS Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2025	J	`jnl`	Gaussian Processes and Reproducing Kernels: Connections and Equivalences. CoRR Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
2025	A	`conf`	Label Distribution Learning using the Squared Neural Family on the Probability Simplex. UAI Daokun Zhang, Russell Tsuchida, Dino Sejdinovic
2025	J	`jnl`	Near-Optimal Approximations for Bayesian Inference in Function Space. CoRR Veit Wild, James Wu, Dino Sejdinovic, Jeremias Knoblauch
2025	J	`jnl`	Squared families: Searching beyond regular probability models. CoRR Russell Tsuchida, Jiawei Liu, Cheng Soon Ong, Dino Sejdinovic
2024	J	`jnl`	A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. J. Mach. Learn. Res. Robert Hu, Dino Sejdinovic, Robin J. Evans
2024	J	`jnl`	An Overview of Causal Inference using Kernel Embeddings. CoRR Dino Sejdinovic
2024	A*	`conf`	Bayesian Adaptive Calibration and Optimal Design. NeurIPS Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla
2024	J	`jnl`	Bayesian Adaptive Calibration and Optimal Design. CoRR Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla
2024	J	`jnl`	Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks. CoRR Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damith C. Ranasinghe, Ehsan Abbasnejad
2024	J	`jnl`	Credal Two-Sample Tests of Epistemic Ignorance. CoRR Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet
2024	J	`jnl`	Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Trans. Mach. Learn. Res. Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
2024	A*	`conf`	Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. AAAI Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
2024	J	`jnl`	Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. CoRR Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
2024	J	`jnl`	Indirect Query Bayesian Optimization with Integrated Feedback. CoRR Mengyan Zhang, Shahine Bouabid, Cheng Soon Ong, Seth R. Flaxman, Dino Sejdinovic
2024	J	`jnl`	Label Distribution Learning using the Squared Neural Family on the Probability Simplex. CoRR Daokun Zhang, Russell Tsuchida, Dino Sejdinovic
2024	A*	`conf`	Neural-Kernel Conditional Mean Embeddings. ICML Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
2024	J	`jnl`	Neural-Kernel Conditional Mean Embeddings. CoRR Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
2023	A*	`conf`	A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. NeurIPS Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch
2023	J	`jnl`	A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. CoRR Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch
2023	A*	`conf`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. NeurIPS Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. CoRR Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
2023	J	`jnl`	Fair Kernel Regression through Cross-Covariance Operators. Trans. Mach. Learn. Res. Adrian Perez-Suay, Paula Gordaliza, Jean-Michel Loubes, Dino Sejdinovic, Gustau Camps-Valls
2023	J	`jnl`	Identifying Pauli spin blockade using deep learning. Quantum 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
2023	A*	`conf`	Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. ICML Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
2023	J	`jnl`	Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. CoRR Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
2023	A*	`conf`	Squared Neural Families: A New Class of Tractable Density Models. NeurIPS Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
2023	J	`jnl`	Squared Neural Families: A New Class of Tractable Density Models. CoRR Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic
2022	J	`jnl`	Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. CoRR Diego Martinez-Taboada, Dino Sejdinovic
2022	J	`jnl`	Bayesian Kernel Two-Sample Testing. J. Comput. Graph. Stat. Qinyi Zhang, Veit Wild, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic
2022	J	`jnl`	Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. CoRR Antonin Schrab, Wittawat Jitkrittum, Zoltán Szabó, Dino Sejdinovic, Arthur Gretton
2022	J	`jnl`	Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. CoRR Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic
2022	A*	`conf`	Explaining Preferences with Shapley Values. NeurIPS Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
2022	J	`jnl`	Explaining Preferences with Shapley Values. CoRR Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic
2022	A*	`conf`	Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. NeurIPS Veit D. Wild, Robert Hu, Dino Sejdinovic
2022	J	`jnl`	Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. CoRR Veit D. Wild, Robert Hu, Dino Sejdinovic
2022	J	`jnl`	Giga-scale Kernel Matrix Vector Multiplication on GPU. CoRR Robert Hu, Dino Sejdinovic, Joan Alexis Glaunès
2022	A*	`conf`	Giga-scale Kernel Matrix-Vector Multiplication on GPU. NeurIPS Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès
2022	J	`jnl`	Identifying Pauli spin blockade using deep learning. CoRR 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
2022	J	`jnl`	Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. Pattern Recognit. Zhu Li, Adrián Pérez-Suay, Gustau Camps-Valls, Dino Sejdinovic
2022	J	`jnl`	Large scale tensor regression using kernels and variational inference. Mach. Learn. Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
2022	A	`conf`	Learning Inconsistent Preferences with Gaussian Processes. AISTATS Siu Lun Chau, Javier González, Dino Sejdinovic
2022	A*	`conf`	RKHS-SHAP: Shapley Values for Kernel Methods. NeurIPS Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic
2022	—	`conf`	Selection, Ignorability and Challenges With Causal Fairness. CLeaR Jake Fawkes, Robin J. Evans, Dino Sejdinovic
2022	J	`jnl`	Selection, Ignorability and Challenges With Causal Fairness. CoRR Jake Fawkes, Robin J. Evans, Dino Sejdinovic
2022	J	`jnl`	Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. CoRR Diego Martinez-Taboada, Dino Sejdinovic
2022	—	`conf`	Spectral Ranking with Covariates. ECML/PKDD (5) Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
2022	A	`conf`	Survival regression with proper scoring rules and monotonic neural networks. AISTATS David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic
2021	A*	`conf`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. NeurIPS Siu Lun Chau, Jean-Francois Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	BayesIMP: Uncertainty Quantification for Causal Data Fusion. CoRR Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	Bridging the reality gap in quantum devices with physics-aware machine learning. CoRR 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
2021	J	`jnl`	Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. CoRR Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
2021	J	`jnl`	Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. CoRR Brandon Severin, Dominic T. Lennon, Leon C. Camenzind, Florian Vigneau, Federico Fedele, D. Jirovec, A. Ballabio, Daniel Chrastina, Giovanni 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
2021	A*	`conf`	Deconditional Downscaling with Gaussian Processes. NeurIPS Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
2021	J	`jnl`	Deconditional Downscaling with Gaussian Processes. CoRR Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic
2021	J	`jnl`	Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. IEEE Trans. Signal Inf. Process. over Networks Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
2021	A*	`conf`	Meta Learning for Causal Direction. AAAI Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu
2021	A	`conf`	Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. AISTATS Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
2021	J	`jnl`	RKHS-SHAP: Shapley Values for Kernel Methods. CoRR Siu Lun Chau, Javier González, Dino Sejdinovic
2021	—	`conf`	Robust Deep Interpretable Features for Binary Image Classification. NLDL Robert Hu, Dino Sejdinovic
2021	J	`jnl`	The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. Patterns 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
2021	J	`jnl`	Time-to-event regression using partially monotonic neural networks. CoRR David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic
2021	J	`jnl`	Towards a Unified Analysis of Random Fourier Features. J. Mach. Learn. Res. Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
2021	J	`jnl`	Unrepresentative big surveys significantly overestimated US vaccine uptake. Nat. Valerie C. Bradley, Shiro Kuriwaki, Michael Isakov, Dino Sejdinovic, Xiao-Li Meng, Seth R. Flaxman
2021	A	`conf`	Variational inference with continuously-indexed normalizing flows. UAI Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
2020	J	`jnl`	A Perspective on Gaussian Processes for Earth Observation. CoRR Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein
2020	J	`jnl`	Benign Overfitting and Noisy Features. CoRR Zhu Li, Weijie J. Su, Dino Sejdinovic
2020	J	`jnl`	Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. CoRR 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
2020	A*	`conf`	Inter-domain Deep Gaussian Processes. ICML Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	Inter-domain Deep Gaussian Processes. CoRR Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal
2020	J	`jnl`	Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint. CoRR Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic
2020	J	`jnl`	Large Scale Tensor Regression using Kernels and Variational Inference. CoRR Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
2020	J	`jnl`	Learning Inconsistent Preferences with Kernel Methods. CoRR Siu Lun Chau, Javier González, Dino Sejdinovic
2020	J	`jnl`	Machine learning enables completely automatic tuning of a quantum device faster than human experts. CoRR 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
2020	J	`jnl`	Meta Learning for Causal Direction. CoRR Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
2020	J	`jnl`	Quantum device fine-tuning using unsupervised embedding learning. CoRR 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
2020	J	`jnl`	Spectral Ranking with Covariates. CoRR Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
2020	J	`jnl`	Variational Inference with Continuously-Indexed Normalizing Flows. CoRR Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
2019	—	`conf`	A Differentially Private Kernel Two-Sample Test. ECML/PKDD (1) Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park
2019	J	`jnl`	Detecting anthropogenic cloud perturbations with deep learning. CoRR Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier
2019	A*	`conf`	Hyperparameter Learning via Distributional Transfer. NeurIPS Ho Chung Leon Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic
2019	J	`jnl`	Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness. CoRR Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls, Dino Sejdinovic
2019	J	`jnl`	Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. CoRR Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic
2019	A*	`conf`	Towards a Unified Analysis of Random Fourier Features. ICML Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
2018	J	`jnl`	A Differentially Private Kernel Two-Sample Test. CoRR Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park
2018	J	`jnl`	A Unified Analysis of Random Fourier Features. CoRR Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic
2018	A	`conf`	Bayesian Approaches to Distribution Regression. AISTATS Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
2018	A*	`conf`	Causal Inference via Kernel Deviance Measures. NeurIPS Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2018	J	`jnl`	Causal Inference via Kernel Deviance Measures. CoRR Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2018	J	`jnl`	Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. CoRR Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur
2018	A*	`conf`	Hamiltonian Variational Auto-Encoder. NeurIPS Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
2018	J	`jnl`	Hamiltonian Variational Auto-Encoder. CoRR Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
2018	J	`jnl`	Hyperparameter Learning via Distributional Transfer. CoRR Ho Chung Leon Law, Peilin Zhao, Junzhou Huang, Dino Sejdinovic
2018	J	`jnl`	Large-scale kernel methods for independence testing. Stat. Comput. Qinyi Zhang, Sarah Filippi, Arthur Gretton, Dino Sejdinovic
2018	J	`jnl`	Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". CoRR François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
2018	A*	`conf`	Variational Learning on Aggregate Outputs with Gaussian Processes. NeurIPS Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu
2018	J	`jnl`	Variational Learning on Aggregate Outputs with Gaussian Processes. CoRR Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim C. D. Lucas, Seth R. Flaxman, Katherine Battle, Kenji Fukumizu
2017	J	`jnl`	Bayesian Distribution Regression. CoRR Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman
2017	—	`conf`	Deep Kernel Machines via the Kernel Reparametrization Trick. ICLR (Workshop) Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2017	A	`conf`	Feature-to-Feature Regression for a Two-Step Conditional Independence Test. UAI Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic
2017	—	`conf`	Kernel Sequential Monte Carlo. ECML/PKDD (1) Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
2017	A	`conf`	Poisson intensity estimation with reproducing kernels. AISTATS Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic
2017	—	`conf`	Testing and Learning on Distributions with Symmetric Noise Invariance. NIPS Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic
2016	A	`conf`	Bayesian Learning of Kernel Embeddings. UAI Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi
2016	J	`jnl`	CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. IEEE Access Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic, Günes Karabulut-Kurt, Camilla Hollanti, Ingo Fischer
2016	J	`jnl`	CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. CoRR Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic, Gunes Karabulut-Kurt, Camilla Hollanti, Ingo Fischer
2016	A*	`conf`	DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. ICML Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2016	J	`jnl`	DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. CoRR Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
2016	J	`jnl`	Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings. CoRR Gianni Franchi, Jesús Angulo, Dino Sejdinovic
2016	B	`conf`	Hyperspectral image classification with support vector machines on kernel distribution embeddings. ICIP Gianni Franchi, Jesús Angulo, Dino Sejdinovic
2016	A	`conf`	K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. AISTATS Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
2016	—	`conf`	Network function computation as a service in future 5G machine type communications. ISTC Dejan Vukobratovic, Dusan Jakovetic, Vitaly Skachek, Dragana Bajovic, Dino Sejdinovic
2016	A	`conf`	Super-Sampling with a Reservoir. UAI Brooks Paige, Dino Sejdinovic, Frank D. Wood
2015	—	`conf`	Compressed Sensing using sparse binary measurements: A rateless coding perspective. SPAWC Dejan Vukobratovic, Dino Sejdinovic, Aleksandra Pizurica
2015	—	`conf`	Fast Two-Sample Testing with Analytic Representations of Probability Measures. NIPS Kacper Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton
2015	—	`conf`	Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton
2015	J	`jnl`	K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. CoRR Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic
2015	A	`conf`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. UAI Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. CoRR Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó
2015	J	`jnl`	Probabilistic Integration. CoRR François-Xavier Briol, Chris J. Oates, Mark A. Girolami, Michael A. Osborne, Dino Sejdinovic
2015	J	`jnl`	Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami
2014	—	`conf`	A Wild Bootstrap for Degenerate Kernel Tests. NIPS Kacper Chwialkowski, Dino Sejdinovic, Arthur Gretton
2014	A*	`conf`	Kernel Adaptive Metropolis-Hastings. ICML Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton
2013	—	`conf`	A Kernel Test for Three-Variable Interactions. NIPS Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
2013	J	`jnl`	Kernel Adaptive Metropolis-Hastings. CoRR Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton
2012	J	`jnl`	Approximate Message Passing under Finite Alphabet Constraints CoRR Andreas Müller, Dino Sejdinovic, Robert J. Piechocki
2012	Misc	`conf`	Approximate message passing under finite alphabet constraints. ICASSP Andreas Müller, Dino Sejdinovic, Robert J. Piechocki
2012	J	`jnl`	Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks CoRR Robert J. Piechocki, Dino Sejdinovic
2012	—	`conf`	Combinatorial channel signature modulation for wireless ad-hoc networks. ICC Robert J. Piechocki, Dino Sejdinovic
2012	J	`jnl`	Equivalence of distance-based and RKHS-based statistics in hypothesis testing CoRR Dino Sejdinovic, Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu
2012	J	`jnl`	Hypothesis testing using pairwise distances and associated kernels (with Appendix) CoRR Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu
2012	A*	`conf`	Hypothesis testing using pairwise distances and associated kernels. ICML Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu
2012	—	`conf`	Optimal kernel choice for large-scale two-sample tests. NIPS Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu
2011	J	`jnl`	Non-parametric change-point detection using string matching algorithms CoRR Oliver Johnson, Dino Sejdinovic, James R. Cruise, Ayalvadi Ganesh, Robert J. Piechocki
2010	—	`conf`	Bayesian sequential compressed sensing in sparse dynamical systems. Allerton Dino Sejdinovic, Christophe Andrieu, Robert J. Piechocki
2010	J	`jnl`	Decentralised distributed fountain coding: asymptotic analysis and design. IEEE Commun. Lett. Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi, Mohamed Ismail
2010	J	`jnl`	Note on Noisy Group Testing: Asymptotic Bounds and Belief Propagation Reconstruction CoRR Dino Sejdinovic, Oliver Johnson
2010	—	`conf`	Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction. Allerton Dino Sejdinovic, Oliver Johnson
2009	B	`conf`	AND-OR tree analysis of distributed LT codes. ITW Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi
2009	J	`jnl`	Expanding window fountain codes for unequal error protection. IEEE Trans. Commun. Dino Sejdinovic, Dejan Vukobratovic, Angela Doufexi, Vojin Senk, Robert J. Piechocki
2009	J	`jnl`	Fountain code design for data multicast with side information. IEEE Trans. Wirel. Commun. Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi, Mohamed Ismail
2009	—	`conf`	Precoded EWF codes for unequal error protection of scalable video. MobiMedia Dejan Vukobratovic, Vladimir Stankovic, Lina Stankovic, Dino Sejdinovic
2009	—	`conf`	Rateless distributed source code design. MobiMedia Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi
2009	J	`jnl`	Scalable Video Multicast Using Expanding Window Fountain Codes. IEEE Trans. Multim. Dejan Vukobratovic, Vladimir Stankovic, Dino Sejdinovic, Lina Stankovic, Zixiang Xiong
2008	A	`conf`	Expanding Window Fountain codes for scalable video multicast. ICME Dejan Vukobratovic, Vladimir Stankovic, Dino Sejdinovic, Lina Stankovic, Zixiang Xiong
2008	—	`conf`	Fountain Coding with Decoder Side Information. ICC Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi, Mohamed Ismail
2008	B	`conf`	Rate Adaptive Binary Erasure Quantization with Dual Fountain Codes. GLOBECOM Dino Sejdinovic, Robert J. Piechocki, Angela Doufexi, Mohamed Ismail
2008	B	`conf`	The Throughput Analysis of Different IR-HARQ Schemes Based on Fountain Codes. WCNC Dino Sejdinovic, Vishakan Ponnampalam, Robert J. Piechocki, Angela Doufexi