Rate Adaptive Binary Erasure Quantization with Dual Fountain Codes. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi; Mohamed Ismail (GLOBECOM, 2008)

Fountain Coding with Decoder Side Information. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi; Mohamed Ismail (ICC, 2008)

Expanding Window Fountain codes for scalable video multicast. - Dejan Vukobratovic; Vladimir Stankovic 001; Dino Sejdinovic; Lina Stankovic; Zixiang Xiong (ICME, 2008)

The Throughput Analysis of Different IR-HARQ Schemes Based on Fountain Codes. - Dino Sejdinovic; Vishakan Ponnampalam; Robert J. Piechocki; Angela Doufexi (WCNC, 2008)

AND-OR tree analysis of distributed LT codes. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi (ITW, 2009)

Rateless distributed source code design. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi (MobiMedia, 2009)

Precoded EWF codes for unequal error protection of scalable video. - Dejan Vukobratovic; Vladimir Stankovic 001; Lina Stankovic; Dino Sejdinovic (MobiMedia, 2009)

Expanding window fountain codes for unequal error protection. - Dino Sejdinovic; Dejan Vukobratovic; Angela Doufexi; Vojin Senk; Robert J. Piechocki (IEEE Trans. Commun., 2009)

Scalable Video Multicast Using Expanding Window Fountain Codes. - Dejan Vukobratovic; Vladimir Stankovic 001; Dino Sejdinovic; Lina Stankovic; Zixiang Xiong (IEEE Trans. Multim., 2009)

Fountain code design for data multicast with side information. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi; Mohamed Ismail (IEEE Trans. Wirel. Commun., 2009)

Bayesian sequential compressed sensing in sparse dynamical systems. - Dino Sejdinovic; Christophe Andrieu; Robert J. Piechocki (Allerton, 2010)

Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction. - Dino Sejdinovic; Oliver Johnson (Allerton, 2010)

Decentralised distributed fountain coding: asymptotic analysis and design. - Dino Sejdinovic; Robert J. Piechocki; Angela Doufexi; Mohamed Ismail (IEEE Commun. Lett., 2010)

Non-parametric change-point detection using string matching algorithms - Oliver Johnson; Dino Sejdinovic; James R. Cruise; Ayalvadi Ganesh 001; Robert J. Piechocki (CoRR, 2011)

Approximate message passing under finite alphabet constraints. - Andreas Müller 008; Dino Sejdinovic; Robert J. Piechocki (ICASSP, 2012)

Combinatorial channel signature modulation for wireless ad-hoc networks. - Robert J. Piechocki; Dino Sejdinovic (ICC, 2012)

Hypothesis testing using pairwise distances and associated kernels. - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (ICML, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Hypothesis testing using pairwise distances and associated kernels (with Appendix) - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (CoRR, 2012)

Equivalence of distance-based and RKHS-based statistics in hypothesis testing - Dino Sejdinovic; Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu (CoRR, 2012)

A Kernel Test for Three-Variable Interactions. - Dino Sejdinovic; Arthur Gretton; Wicher Bergsma (NIPS, 2013)

Kernel Adaptive Metropolis-Hastings. - Dino Sejdinovic; Heiko Strathmann; Maria Lomeli Garcia; Christophe Andrieu; Arthur Gretton (ICML, 2014)

A Wild Bootstrap for Degenerate Kernel Tests. - Kacper Chwialkowski; Dino Sejdinovic; Arthur Gretton (NIPS, 2014)

Fast Two-Sample Testing with Analytic Representations of Probability Measures. - Kacper Chwialkowski; Aaditya Ramdas; Dino Sejdinovic; Arthur Gretton (NIPS, 2015)

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. - Heiko Strathmann; Dino Sejdinovic; Samuel Livingstone; Zoltán Szabó 001; Arthur Gretton (NIPS, 2015)

Compressed Sensing using sparse binary measurements: A rateless coding perspective. - Dejan Vukobratovic; Dino Sejdinovic; Aleksandra Pizurica (SPAWC, 2015)

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages. - Wittawat Jitkrittum; Arthur Gretton; Nicolas Heess; S. M. Ali Eslami; Balaji Lakshminarayanan; Dino Sejdinovic; Zoltán Szabó 001 (UAI, 2015)

Probabilistic Integration. - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2015)

Unbiased Bayes for Big Data: Paths of Partial Posteriors. - Heiko Strathmann; Dino Sejdinovic; Mark A. Girolami (CoRR, 2015)

K2-ABC: Approximate Bayesian Computation with Kernel Embeddings. - Mijung Park; Wittawat Jitkrittum; Dino Sejdinovic (AISTATS, 2016)

Hyperspectral image classification with support vector machines on kernel distribution embeddings. - Gianni Franchi; Jesús Angulo; Dino Sejdinovic (ICIP, 2016)

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (ICML, 2016)

Network function computation as a service in future 5G machine type communications. - Dejan Vukobratovic; Dusan Jakovetic; Vitaly Skachek; Dragana Bajovic; Dino Sejdinovic (ISTC, 2016)

Bayesian Learning of Kernel Embeddings. - Seth R. Flaxman; Dino Sejdinovic; John P. Cunningham; Sarah Filippi (UAI, 2016)

Super-Sampling with a Reservoir. - Brooks Paige; Dino Sejdinovic; Frank D. Wood (UAI, 2016)

CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. - Dejan Vukobratovic; Dusan Jakovetic; Vitaly Skachek; Dragana Bajovic; Dino Sejdinovic; Günes Karabulut-Kurt; Camilla Hollanti; Ingo Fischer (IEEE Access, 2016)

Poisson intensity estimation with reproducing kernels. - Seth R. Flaxman; Yee Whye Teh; Dino Sejdinovic (AISTATS, 2017)

Deep Kernel Machines via the Kernel Reparametrization Trick. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (ICLR (Workshop), 2017)

Testing and Learning on Distributions with Symmetric Noise Invariance. - Ho Chung Leon Law; Christopher Yau; Dino Sejdinovic (NIPS, 2017)

Kernel Sequential Monte Carlo. - Ingmar Schuster; Heiko Strathmann; Brooks Paige; Dino Sejdinovic (ECML/PKDD (1), 2017)

Feature-to-Feature Regression for a Two-Step Conditional Independence Test. - Qinyi Zhang; Sarah Filippi; Seth R. Flaxman; Dino Sejdinovic (UAI, 2017)

Bayesian Distribution Regression. - Ho Chung Leon Law; Danica J. Sutherland; Dino Sejdinovic; Seth R. Flaxman (CoRR, 2017)

Bayesian Approaches to Distribution Regression. - Ho Chung Leon Law; Danica J. Sutherland; Dino Sejdinovic; Seth R. Flaxman (AISTATS, 2018)

Hamiltonian Variational Auto-Encoder. - Anthony L. Caterini; Arnaud Doucet; Dino Sejdinovic (NeurIPS, 2018)

Variational Learning on Aggregate Outputs with Gaussian Processes. - Ho Chung Leon Law; Dino Sejdinovic; Ewan Cameron; Tim C. D. Lucas; Seth R. Flaxman; Katherine Battle; Kenji Fukumizu (NeurIPS, 2018)

Causal Inference via Kernel Deviance Measures. - Jovana Mitrovic; Dino Sejdinovic; Yee Whye Teh (NeurIPS, 2018)

A Unified Analysis of Random Fourier Features. - Zhu Li; Jean-Francois Ton; Dino Oglic; Dino Sejdinovic (CoRR, 2018)

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2018)

Hyperparameter Learning via Distributional Transfer. - Ho Chung Leon Law; Peilin Zhao; Junzhou Huang; Dino Sejdinovic (CoRR, 2018)

Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?". - François-Xavier Briol; Chris J. Oates; Mark A. Girolami; Michael A. Osborne; Dino Sejdinovic (CoRR, 2018)

Large-scale kernel methods for independence testing. - Qinyi Zhang; Sarah Filippi; Arthur Gretton; Dino Sejdinovic (Stat. Comput., 2018)

Hyperparameter Learning via Distributional Transfer. - Ho Chung Leon Law; Peilin Zhao; Leung Sing Chan; Junzhou Huang; Dino Sejdinovic (NeurIPS, 2019)

A Differentially Private Kernel Two-Sample Test. - Anant Raj; Ho Chung Leon Law; Dino Sejdinovic; Mijung Park (ECML/PKDD (1), 2019)

Detecting anthropogenic cloud perturbations with deep learning. - Duncan Watson-Parris; Samuel Sutherland; Matthew Christensen; Anthony L. Caterini; Dino Sejdinovic; Philip Stier (CoRR, 2019)

Inter-domain Deep Gaussian Processes. - Tim G. J. Rudner; Dino Sejdinovic; Yarin Gal (ICML, 2020)

Machine learning enables completely automatic tuning of a quantum device faster than human experts. - 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; et al. (CoRR, 2020)

Quantum device fine-tuning using unsupervised embedding learning. - 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 (CoRR, 2020)

Learning Inconsistent Preferences with Kernel Methods. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (CoRR, 2020)

A Perspective on Gaussian Processes for Earth Observation. - Gustau Camps-Valls; Dino Sejdinovic; Jakob Runge; Markus Reichstein (CoRR, 2020)

Benign Overfitting and Noisy Features. - Zhu Li; Weijie J. Su; Dino Sejdinovic (CoRR, 2020)

Deep Reinforcement Learning for Efficient Measurement of Quantum Devices. - 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 (CoRR, 2020)

Meta Learning for Causal Direction. - Jean-François Ton; Dino Sejdinovic; Kenji Fukumizu (AAAI, 2021)

Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. - Jean-Francois Ton; Lucian Chan; Yee Whye Teh; Dino Sejdinovic (AISTATS, 2021)

Deconditional Downscaling with Gaussian Processes. - Siu Lun Chau; Shahine Bouabid; Dino Sejdinovic (NeurIPS, 2021)

BayesIMP: Uncertainty Quantification for Causal Data Fusion. - Siu Lun Chau; Jean-Francois Ton; Javier González 002; Yee Whye Teh; Dino Sejdinovic (NeurIPS, 2021)

Robust Deep Interpretable Features for Binary Image Classification. - Robert Hu; Dino Sejdinovic (NLDL, 2021)

Variational inference with continuously-indexed normalizing flows. - Anthony L. Caterini; Robert Cornish; Dino Sejdinovic; Arnaud Doucet (UAI, 2021)

Time-to-event regression using partially monotonic neural networks. - David Rindt; Robert Hu; David Steinsaltz; Dino Sejdinovic (CoRR, 2021)

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. - Veit Wild; Motonobu Kanagawa; Dino Sejdinovic (CoRR, 2021)

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. - 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; et al. (CoRR, 2021)

RKHS-SHAP: Shapley Values for Kernel Methods. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (CoRR, 2021)

Bridging the reality gap in quantum devices with physics-aware machine learning. - 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 (CoRR, 2021)

Towards a Unified Analysis of Random Fourier Features. - Zhu Li; Jean-Francois Ton; Dino Oglic; Dino Sejdinovic (J. Mach. Learn. Res., 2021)

Unrepresentative big surveys significantly overestimated US vaccine uptake. - Valerie C. Bradley; Shiro Kuriwaki; Michael Isakov; Dino Sejdinovic; Xiao-Li Meng; Seth R. Flaxman (Nat., 2021)

The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. - 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; et al. (Patterns, 2021)

Kernel-Based Graph Learning From Smooth Signals: A Functional Viewpoint. - Xingyue Pu; Siu Lun Chau; Xiaowen Dong 001; Dino Sejdinovic (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Learning Inconsistent Preferences with Gaussian Processes. - Siu Lun Chau; Javier González 002; Dino Sejdinovic (AISTATS, 2022)

Survival regression with proper scoring rules and monotonic neural networks. - David Rindt; Robert Hu; David Steinsaltz; Dino Sejdinovic (AISTATS, 2022)

Selection, Ignorability and Challenges With Causal Fairness. - Jake Fawkes; Robin J. Evans 002; Dino Sejdinovic (CLeaR, 2022)

RKHS-SHAP: Shapley Values for Kernel Methods. - Siu Lun Chau; Robert Hu; Javier González 002; Dino Sejdinovic (NeurIPS, 2022)

Explaining Preferences with Shapley Values. - Robert Hu; Siu Lun Chau; Jaime Ferrando Huertas; Dino Sejdinovic (NeurIPS, 2022)

Giga-scale Kernel Matrix-Vector Multiplication on GPU. - Robert Hu; Siu Lun Chau; Dino Sejdinovic; Joan Glaunès (NeurIPS, 2022)

Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. - Veit D. Wild; Robert Hu; Dino Sejdinovic (NeurIPS, 2022)

Spectral Ranking with Covariates. - Siu Lun Chau; Mihai Cucuringu; Dino Sejdinovic (ECML/PKDD (5), 2022)

Giga-scale Kernel Matrix Vector Multiplication on GPU. - Robert Hu; Dino Sejdinovic; Joan Alexis Glaunès (CoRR, 2022)

Discussion of 'Multiscale Fisher's Independence Test for Multivariate Dependence'. - Antonin Schrab; Wittawat Jitkrittum; Zoltán Szabó 001; Dino Sejdinovic; Arthur Gretton (CoRR, 2022)

Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. - Diego Martinez-Taboada; Dino Sejdinovic (CoRR, 2022)

Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. - Diego Martinez-Taboada; Dino Sejdinovic (CoRR, 2022)

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap. - Jake Fawkes; Robert Hu; Robin J. Evans 002; Dino Sejdinovic (CoRR, 2022)

Large scale tensor regression using kernels and variational inference. - Robert Hu; Geoff K. Nicholls; Dino Sejdinovic (Mach. Learn., 2022)

Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. - Zhu Li; Adrián Pérez-Suay; Gustau Camps-Valls; Dino Sejdinovic (Pattern Recognit., 2022)

Bayesian Kernel Two-Sample Testing. - Qinyi Zhang; Veit Wild; Sarah Filippi; Seth R. Flaxman; Dino Sejdinovic (J. Comput. Graph. Stat., 2022)

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. - Shahine Bouabid; Jake Fawkes; Dino Sejdinovic (ICML, 2023)

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. - Siu Lun Chau; Krikamol Muandet; Dino Sejdinovic (NeurIPS, 2023)

Squared Neural Families: A New Class of Tractable Density Models. - Russell Tsuchida; Cheng Soon Ong; Dino Sejdinovic (NeurIPS, 2023)

A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. - Veit David Wild; Sahra Ghalebikesabi; Dino Sejdinovic; Jeremias Knoblauch (NeurIPS, 2023)

Fair Kernel Regression through Cross-Covariance Operators. - Adrian Perez-Suay; Paula Gordaliza; Jean-Michel Loubes; Dino Sejdinovic; Gustau Camps-Valls (Trans. Mach. Learn. Res., 2023)

Identifying Pauli spin blockade using deep learning. - 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 (Quantum, 2023)

Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. - Russell Tsuchida; Cheng Soon Ong; Dino Sejdinovic (AAAI, 2024)

Neural-Kernel Conditional Mean Embeddings. - Eiki Shimizu; Kenji Fukumizu; Dino Sejdinovic (ICML, 2024)

Bayesian Adaptive Calibration and Optimal Design. - Rafael Oliveira 001; Dino Sejdinovic; David Howard; Edwin V. Bonilla (NeurIPS, 2024)

Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks. - Bao Gia Doan; Afshar Shamsi; Xiao-Yu Guo; Arash Mohammadi 001; Hamid Alinejad-Rokny; Dino Sejdinovic; Damith C. Ranasinghe; Ehsan Abbasnejad (CoRR, 2024)

Credal Two-Sample Tests of Epistemic Ignorance. - Siu Lun Chau; Antonin Schrab; Arthur Gretton; Dino Sejdinovic; Krikamol Muandet (CoRR, 2024)

An Overview of Causal Inference using Kernel Embeddings. - Dino Sejdinovic (CoRR, 2024)

Indirect Query Bayesian Optimization with Integrated Feedback. - Mengyan Zhang; Shahine Bouabid; Cheng Soon Ong; Seth R. Flaxman; Dino Sejdinovic (CoRR, 2024)

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. - Robert Hu; Dino Sejdinovic; Robin J. Evans 002 (J. Mach. Learn. Res., 2024)

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. - Jake Fawkes; Robert Hu; Robin J. Evans 002; Dino Sejdinovic (Trans. Mach. Learn. Res., 2024)

Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks. - Bao Gia Doan; Afshar Shamsi; Xiao-Yu Guo; Arash Mohammadi 001; Hamid Alinejad-Rokny; Dino Sejdinovic; Damien Teney; Damith C. Ranasinghe; Ehsan Abbasnejad (AAAI, 2025)

Credal Two-Sample Tests of Epistemic Uncertainty. - Siu Lun Chau; Antonin Schrab; Arthur Gretton; Dino Sejdinovic; Krikamol Muandet (AISTATS, 2025)

Label Distribution Learning using the Squared Neural Family on the Probability Simplex. - Daokun Zhang; Russell Tsuchida; Dino Sejdinovic (UAI, 2025)

All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. - Peter Moskvichev; Dino Sejdinovic (CoRR, 2025)

Near-Optimal Approximations for Bayesian Inference in Function Space. - Veit Wild; James Wu; Dino Sejdinovic; Jeremias Knoblauch (CoRR, 2025)

Squared families: Searching beyond regular probability models. - Russell Tsuchida; Jiawei Liu; Cheng Soon Ong; Dino Sejdinovic (CoRR, 2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2025)