Research Interests
Machine learning Statistical data science Computational statistics Artificial IntelligenceProf Dino Sejdinovic
Professor
School of Computer Science and Information Technology
College of Engineering and Information Technology
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
Dino Sejdinovic is a Professor in the School of Mathematical Sciences, Adelaide University. He was previously a Lecturer and an Associate Professor at the Department of Statistics, University of Oxford (2014-2022). He held postdoctoral positions at the Gatsby Computational Neuroscience Unit, University College London (2011-2014) and at the Institute for Statistical Science, University of Bristol (2009-2011). He received a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006).
My research spans a variety of topics at the interface between statistical methodology and machine learning, including:
- Large-scale nonparametric and kernel methods,
- Robust and trustworthy machine learning,
- Multiresolution data and data fusion,
- Measures of dependence and multivariate interaction.
| Date | Position | Institution name |
|---|---|---|
| 2022 - ongoing | Professor | Adelaide University |
| 2016 - 2022 | Associate Professor | University of Oxford |
| 2014 - 2016 | Lecturer | University of Oxford |
| 2011 - 2014 | Postdoctoral Fellow | University College London |
| 2009 - 2011 | Brunel Postdoctoral Fellow | University of Bristol |
| Date | Institution name | Country | Title |
|---|---|---|---|
| 2009 | University of Bristol | United Kingdom | PhD |
| 2006 | University of Sarajevo | Bosnia and Herzegovina | Dipl.Math.-Inf. |
| Year | Citation |
|---|---|
| 2025 | Dan, S., Ling, Z., Chen, Y., Tegegne, J., Jaeger, V. K., Karch, A., . . . Bhatt, S. (2025). Addressing survey fatigue bias in longitudinal social contact studies to improve pandemic preparedness. Scientific Reports, 15(1), 17935. |
| 2025 | Matabuena, M., Vidal, J. C., Padilla, O. H. M., & Sejdinovic, D. (2025). Kernel biclustering algorithm in Hilbert spaces. Advances in Data Analysis and Classification, 42 pages. |
| 2024 | Craig, D. L., Moon, H., Fedele, F., Lennon, D. T., Van Straaten, B., Vigneau, F., . . . Ares, N. (2024). Bridging the Reality Gap in Quantum Devices with Physics-Aware Machine Learning. Physical Review X, 14(1). Scopus10 |
| 2024 | Bouabid, S., Sejdinovic, D., & Watson-Parris, D. (2024). FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 16(6), 53 pages. Scopus7 WoS8 |
| 2024 | Fawkes, J., Hu, R., Evans, R. J., & Sejdinovic, D. (2024). Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Transactions on Machine Learning Research, 2024. Scopus1 |
| 2024 | Hu, R., Sejdinovic, D., & Evans, R. J. (2024). A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. Journal of Machine Learning Research, 25, 160-1-160-56. |
| 2024 | Bouabid, S., Watson-Parris, D., Stefanović, S., Nenes, A., & Sejdinovic, D. (2024). Aerosol optical depth disaggregation: toward global aerosol vertical profiles. Environmental Data Science, 3, e16-1-e16-33. |
| 2024 | Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., . . . Ares, N. (2024). Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports, 14(1), 17281. Scopus6 Europe PMC2 |
| 2024 | Lenhardt, J., Quaas, J., & Sejdinovic, D. (2024). Marine cloud base height retrieval from MODIS cloud properties using machine learning. ATMOSPHERIC MEASUREMENT TECHNIQUES, 17(18), 5655-5677. |
| 2023 | Chau, S. L., Cucuringu, M., & Sejdinovic, D. (2023). Spectral Ranking with Covariates. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13717 LNAI, 70-86. Scopus4 WoS3 |
| 2023 | Perez-Suay, A., Gordaliza, P., Loubes, J. -M., Sejdinovic, D., & Camps-Valls, G. (2023). Fair Kernel Regression through Cross-Covariance Operators. Transactions on Machine Learning Research, 2023. Scopus2 |
| 2023 | Hu, R., & Sejdinovic, D. (2023). Towards Deep Interpretable Features. Journal of Computational Mathematics and Data Science, 6, 100067. Scopus2 |
| 2023 | Li, Z., Su, W. J., & Sejdinovic, D. (2023). Benign Overfitting and Noisy Features. Journal of the American Statistical Association, 118(544), 2876-2888. Scopus7 WoS8 |
| 2023 | Schuff, J., Lennon, D. T., Geyer, S., Craig, D. L., Fedele, F., Vigneau, F., . . . Ares, N. (2023). Identifying Pauli spin blockade using deep learning. Quantum, 7, 1077. Scopus4 WoS5 |
| 2022 | Wild, V. D., Hu, R., & Sejdinovic, D. (2022). Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. Advances in Neural Information Processing Systems, 35. Scopus13 |
| 2022 | Hu, R., Chau, S. L., Sejdinovic, D., & Glaunès, J. A. (2022). Giga-scale Kernel Matrix-Vector Multiplication on GPU. Advances in Neural Information Processing Systems, 35. Scopus1 |
| 2022 | Schrab, A., Jitkrittum, W., Szabo, Z., Sejdinovic, D., & Gretton, A. (2022). Discussion of 'Multi-scale Fisher's independence test for multivariate dependence'. BIOMETRIKA, 109(3), 597-603. Scopus1 |
| 2022 | Hu, R., Chau, S. L., Huertas, J. F., & Sejdinovic, D. (2022). Explaining Preferences with Shapley Values. Advances in Neural Information Processing Systems, 35. Scopus9 |
| 2022 | Chau, S. L., Hu, R., Gonzalez, J., & Sejdinovic, D. (2022). RKHS-SHAP: Shapley Values for Kernel Methods. Advances in Neural Information Processing Systems, 35. Scopus24 |
| 2022 | Li, Z., Pérez-Suay, A., Camps-Valls, G., & Sejdinovic, D. (2022). Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness. Pattern Recognition, 132, 108922. Scopus7 WoS5 |
| 2022 | Zhang, Q., Wild, V., Filippi, S., Flaxman, S., & Sejdinovic, D. (2022). Bayesian Kernel Two-Sample Testing. Journal of Computational and Graphical Statistics, 31(4), 1164-1176. Scopus3 WoS14 |
| 2022 | Hu, R., Nicholls, G. K., & Sejdinovic, D. (2022). Large scale tensor regression using kernels and variational inference. Machine Learning, 111(7), 2663-2713. Scopus2 WoS1 |
| 2022 | Cortés-Andrés, J., Camps-Valls, G., Sippel, S., Székely, E., Sejdinovic, D., Diaz, E., . . . Reichstein, M. (2022). Physics-aware nonparametric regression models for Earth data analysis. Environmental Research Letters, 17(5), 054034. Scopus12 WoS12 |
| 2022 | Fawkes, J., Evans, R. J., & Sejdinovic, D. (2022). Selection, Ignorability and Challenges with Causal Fairness. Proceedings of Machine Learning Research, 177, 275-289. Scopus7 |
| 2022 | Chau, S. L., Gonzalez, J., & Sejdinovic, D. (2022). Learning Inconsistent Preferences with Gaussian Processes. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151, 151, 16 pages. Scopus5 WoS1 |
| 2021 | Craig, D. L., Moon, H., Fedele, F., Lennon, D. T., Straaten, B. V., Vigneau, F., . . . Ares, N. (2021). Bridging the reality gap in quantum devices with physics-aware machine learning. |
| 2021 | Rindt, D., Sejdinovic, D., & Steinsaltz, D. (2021). Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC. Stat, 10(1), 10 pages. Scopus7 WoS6 |
| 2021 | Bradley, V. C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X. L., & Flaxman, S. (2021). Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature, 600(7890), 695-700. Scopus183 WoS167 Europe PMC117 |
| 2021 | Fernández, T., Gretton, A., Rindt, D., & Sejdinovic, D. (2021). A Kernel Log-Rank Test of Independence for Right-Censored Data. Journal of the American Statistical Association, 118(542), 1-12. Scopus4 WoS4 |
| 2021 | Nguyen, V., Orbell, S. B., Lennon, D. T., Moon, H., Vigneau, F., Camenzind, L. C., . . . Ares, N. (2021). Deep reinforcement learning for efficient measurement of quantum devices. npj Quantum Information, 7(1), 9 pages. Scopus33 WoS35 |
| 2021 | Li, Z., Ton, J. F., Oglic, D., & Sejdinovic, D. (2021). Towards a unified analysis of random fourier features. Journal of Machine Learning Research, 22(1), 4887-4937. Scopus50 WoS35 |
| 2021 | Pu, X., Chau, S. L., Dong, X., & Sejdinovic, D. (2021). Kernel-Based Graph Learning from Smooth Signals: A Functional Viewpoint. IEEE Transactions on Signal and Information Processing over Networks, 7, 192-207. Scopus21 WoS21 |
| 2021 | Blair, G. S., Bassett, R., Bastin, L., Beevers, L., Borrajo, M. I., Brown, M., . . . Watkins, J. (2021). The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord. Patterns, 2(1), 100156. Scopus6 WoS7 |
| 2021 | Rindt, D., Sejdinovic, D., & Steinsaltz, D. (2021). A kernel- And optimal transport- based test of independence between covariates and right-censored lifetimes. International Journal of Biostatistics, 17(2), 331-348. Scopus3 WoS3 Europe PMC1 |
| 2020 | Van Esbroeck, N. M., Lennon, D. T., Moon, H., Nguyen, V., Vigneau, F., Camenzind, L. C., . . . Ares, N. (2020). Quantum device fine-tuning using unsupervised embedding learning. New Journal of Physics, 22(9), 9 pages. Scopus22 WoS20 |
| 2020 | Moon, H., Lennon, D. T., Kirkpatrick, J., van Esbroeck, N. M., Camenzind, L. C., Yu, L., . . . Ares, N. (2020). Machine learning enables completely automatic tuning of a quantum device faster than human experts. Nature Communications, 11(1), 10 pages. Scopus66 WoS67 Europe PMC14 |
| 2020 | Sejdinovic, D. (2020). Discussion of "Functional Models for Time-Varying Random Objects'' by Dubey and Müller. |
| 2019 | Watson-Parris, D., Sutherland, S., Christensen, M., Caterini, A., Sejdinovic, D., & Stier, P. (2019). Detecting anthropogenic cloud perturbations with deep learning. |
| 2019 | Briol, F. X., Oates, C. J., Girolami, M., Osborne, M. A., & Sejdinovic, D. (2019). Rejoinder: Probabilistic integration: A role in statistical computation?. Statistical Science, 34(1), 38-42. Scopus7 WoS2 |
| 2019 | Camps-Valls, G., Sejdinovic, D., Runge, J., & Reichstein, M. (2019). A perspective on Gaussian processes for Earth observation. National Science Review, 6(4), 616-618. Scopus50 WoS47 Europe PMC17 |
| 2019 | Briol, F. X., Oates, C. J., Girolami, M., Osborne, M. A., & Sejdinovic, D. (2019). Probabilistic integration: A role in statistical computation?. Statistical Science, 34(1), 1-22. Scopus116 WoS105 |
| 2019 | Runge, J., Nowack, P., Kretschmer, M., Flaxman, S., & Sejdinovic, D. (2019). Detecting and quantifying causal associations in large nonlinear time series datasets. Science Advances, 5(11), 15 pages. Scopus758 WoS679 Europe PMC161 |
| 2018 | Ton, J. F., Flaxman, S., Sejdinovic, D., & Bhatt, S. (2018). Spatial mapping with Gaussian processes and nonstationary Fourier features. Spatial Statistics, 28, 59-78. Scopus43 WoS38 Europe PMC10 |
| 2018 | Zhang, Q., Filippi, S., Gretton, A., & Sejdinovic, D. (2018). Large-scale kernel methods for independence testing. Statistics and Computing, 28(1), 113-130. Scopus87 WoS72 |
| 2017 | Flaxman, S., Teh, Y. W., & Sejdinovic, D. (2017). Poisson intensity estimation with reproducing kernels. Electronic Journal of Statistics, 11(2), 5081-5104. Scopus17 WoS14 |
| 2016 | Vukobratovic, D., Jakovetic, D., Skachek, V., Bajovic, D., Sejdinovic, D., Karabulut Kurt, G., . . . Fischer, I. (2016). CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT. IEEE Access, 4, 3360-3378. Scopus31 WoS20 |
| 2015 | Kurth-Nelson, Z., Barnes, G., Sejdinovic, D., Dolan, R., & Dayan, P. (2015). Temporal structure in associative retrieval. Elife, 2015(4), 18 pages. Scopus51 WoS47 Europe PMC54 |
| 2014 | Johnson, O., Sejdinovic, D., Cruise, J., Piechocki, R., & Ganesh, A. (2014). Non-Parametric Change-Point Estimation using String Matching Algorithms. Methodology and Computing in Applied Probability, 16(4), 987-1008. Scopus1 WoS1 |
| 2013 | Sejdinovic, D., Sriperumbudur, B., Gretton, A., & Fukumizu, K. (2013). Equivalence of distance-based and RKHS-based statistics in hypothesis testing. Annals of Statistics, 41(5), 2263-2291. Scopus535 WoS469 |
| 2010 | Sejdinović, D., Piechocki, R., Doufexi, A., & Ismail, M. (2010). Decentralised distributed fountain coding: Asymptotic analysis and design. IEEE Communications Letters, 14(1), 42-44. Scopus18 WoS17 |
| 2009 | Sejdinović, D., Piechocki, R. J., Doufexi, A., & Ismail, M. (2009). Fountain code design for data multicast with side information. IEEE Transactions on Wireless Communications, 8(10), 5155-5165. Scopus19 WoS18 |
| 2009 | Sejdinović, D., Vukobratović, D., Doufexi, A., Šenk, V., & Piechocki, R. J. (2009). Expanding window fountain codes for unequal error protection. IEEE Transactions on Communications, 57(9), 2510-2516. Scopus158 WoS125 |
| 2009 | Vukobratović, D., Stankoyić, V., Sejdinović, D., Stanković, L., & Xiong, Z. (2009). Scalable video multicast using expanding window fountain codes. IEEE Transactions on Multimedia, 11(6), 1094-1104. Scopus124 WoS106 |
| - | Sejdinovic, D. (2025). Instrumental identification of interventional distribution via moment recovery in a parametric outcome model. Asian Journal of Economics and Banking, 1-10. |
| Year | Citation |
|---|---|
| 2026 | Moskvichev, P., & Sejdinovic, D. (2026). All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. In Lecture Notes in Computer Science (Vol. 16370 LNAI, pp. 274-287). Springer Nature Singapore. DOI |
| Year | Citation |
|---|---|
| 2025 | Tsuchida, R., Liu, J., Ong, C. S., & Sejdinovic, D. (2025). Squared families are useful conjugate priors. In Advances in Neural Information Processing Systems. San Diego. |
| 2025 | Doan, B. G., Shamsi, A., Guo, X. Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., . . . Abbasnejad, E. (2025). Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks. In T. Walsh, J. Shah, & Z. Kolter (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence Vol. 39 (pp. 16298-16307). Philadelphia, Pennsylvania: Association for the Advancement of Artificial Intelligence (AAAI). DOI Scopus3 WoS1 |
| 2025 | Moskvichev, P., & Sejdinovic, D. (2025). All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. |
| 2025 | Chau, S. L., Schrab, A., Gretton, A., Sejdinovic, D., & Muandet, K. (2025). Credal Two-Sample Tests of Epistemic Uncertainty. In Y. Li, S. Mandt, S. Agrawal, & E. Khan (Eds.), Proceedings of Machine Learning Research Vol. 258 (pp. 127-135). THAILAND: JMLR-JOURNAL MACHINE LEARNING RESEARCH. Scopus3 |
| 2025 | Zhang, D., Tsuchida, R., & Sejdinovic, D. (2025). Label Distribution Learning using the Squared Neural Family on the Probability Simplex. In S. Chiappa, & S. Magliacane (Eds.), Proceedings of Machine Learning Research Vol. 286 (pp. 4872-4881). BRAZIL, Rio de Janeiro: JMLR-JOURNAL MACHINE LEARNING RESEARCH. |
| 2024 | Oliveira, R., Sejdinovic, D., Howard, D., & Bonilla, E. V. (2024). Bayesian Adaptive Calibration and Optimal Design. In Advances in Neural Information Processing Systems Vol. 37. Vancouver: Neural information processing systems foundation Original language. |
| 2024 | Shimizu, E., Fukumizu, K., & Sejdinovic, D. (2024). Neural-Kernel Conditional Mean Embeddings. In Proceedings of Machine Learning Research Vol. 235 (pp. 45040-45059). Vienna: ML Research Press. |
| 2024 | Tsuchida, R., Ong, C. S., & Sejdinovic, D. (2024). Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 20559-20566). Online: Association for the Advancement of Artificial Intelligence (AAAI). DOI Scopus3 WoS2 |
| 2023 | Bouabid, S., Fawkes, J., & Sejdinovic, D. (2023). Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge. In Proceedings of Machine Learning Research Vol. 202 (pp. 2885-2913). Online: MLResearch Press. |
| 2023 | Chau, S. L., Muandet, K., & Sejdinovic, D. (2023). Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS) Vol. 36 (pp. 27 pages). Online: Neural Information Processing Systems Foundation, Inc. (NeurIPS). Scopus9 |
| 2023 | Wild, V. D., Ghalebikesabi, S., Sejdinovic, D., & Knoblauch, J. (2023). A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods. In Advances in Neural Information Processing Systems (NeurIPS) Vol. 36. Online: Neural information processing systems foundation. Scopus10 |
| 2023 | Tsuchida, R., Ong, C. S., & Sejdinovic, D. (2023). Squared Neural Families: A New Class of Tractable Density Models. In Advances in Neural Information Processing Systems (NeurIPS) Vol. 36 (pp. 26 pages). Online: Neural information processing systems foundation. Scopus7 |
| 2022 | Wild, V. D., Hu, R., & Sejdinovic, D. (2022). Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35, NEURIPS 2022 (pp. 15 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). WoS4 |
| 2022 | Hu, R., Chau, S. L., Huertas, J. F., & Sejdinovic, D. (2022). Explaining Preferences with Shapley Values. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) (pp. 14 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). |
| 2022 | Hu, R., Chau, S. L., Sejdinovic, D., & Glaunes, J. A. (2022). Giga-scale Kernel Matrix-Vector Multiplication on GPU. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) (pp. 13 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). |
| 2022 | Rindt, D., Hu, R., Steinsaltz, D., & Sejdinovic, D. (2022). Survival Regression with Proper Scoring Rules and Monotonic Neural Networks. In G. Camps-Valls, F. J. R. Ruiz, & I. Valera (Eds.), Proceedings of The 25th International Conference on Artificial Intelligence and Statistics Vol. 151 (pp. 1190-1205). Virtual Conference: JMLR-JOURNAL MACHINE LEARNING RESEARCH. Scopus31 WoS14 |
| 2022 | Chau, S. L., Hu, R., Gonzalez, J., & Sejdinovic, D. (2022). RKHS-SHAP: Shapley Values for Kernel Methods. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) (pp. 14 pages). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). |
| 2021 | Caterini, A., Cornish, R., Sejdinovic, D., & Doucet, A. (2021). Variational Inference with Continuously-Indexed Normalizing Flows. In C. DeCampos, & M. H. Maathuis (Eds.), UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, VOL 161 Vol. 161 (pp. 44-53). ELECTR NETWORK: JMLR-JOURNAL MACHINE LEARNING RESEARCH. WoS6 |
| 2021 | Ton, J. -F., Chan, L., Teh, Y. W., & Sejdinovic, D. (2021). Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. In A. Banerjee, & K. Fukumizu (Eds.), 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) Vol. 130 (pp. 11 pages). Online virtual: PMLR Publisher. Scopus8 WoS3 |
| 2021 | Chau, S. L., Bouabid, S., & Sejdinovic, D. (2021). Deconditional Downscaling with Gaussian Processes. In M. Ranzato (Ed.), Advances in Neural Information Processing Systems Vol. 22 (pp. 17813-17825). Online conference, Canada: Neural Information Processing Systems Foundation, Inc. (NeurIPS). Scopus13 |
| 2021 | Chau, S. L., Ton, J. F., González, J., Teh, Y. W., & Sejdinovic, D. (2021). BAYESIMP: Uncertainty Quantification for Causal Data Fusion. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), Proceedings of Advances in Neural Information Processing Systems 34 Vol. 5 (pp. 3466-3477). Online: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). Scopus10 |
| 2021 | Ton, J. F., Sejdinovic, D., & Fukumizu, K. (2021). Meta Learning for Causal Direction. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 Vol. 11B (pp. 9897-9905). Online: AAAI. DOI Scopus14 WoS13 |
| 2021 | Caterini, A., Cornish, R., Sejdinovic, D., & Doucet, A. (2021). Variational Inference with Continuously-Indexed Normalizing Flows. In C. de Campos, & M. H. Maathuis (Eds.), Proceedings of Machine Learning Research Vol. 161 (pp. 44-53). online: AUAI Press. Scopus11 |
| 2020 | Rudner, T. G. J., Sejdinovic, D., & Gal, Y. (2020). Inter-domain deep gaussian processes. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020 Vol. PartF168147-11 (pp. 8256-8264). Vienna, Austria: Journal of Machine Learning Research. Scopus4 |
| 2020 | Raj, A., Law, H. C. L., Sejdinovic, D., & Park, M. (2020). A Differentially Private Kernel Two-Sample Test. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11906 LNAI (pp. 697-724). Online: Springer International Publishing. DOI Scopus4 WoS3 |
| 2019 | Law, H. C. L., Zhao, P., Chan, L., Huang, J., & Sejdinovic, D. (2019). Hyperparameter learning via distributional transfer. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alche-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (NeurIPS 2019) Vol. 32 (pp. 23 pages). Washington, D.C., USA: American Chemical Society (ACS). Scopus14 WoS5 |
| 2019 | Li, Z., Ton, J. F., Oglic, D., & Sejdinovic, D. (2019). Towards a unified analysis of random Fourier features. In K. Chaudhuri, & R. Salakhutdinov (Eds.), 36th International Conference on Machine Learning, ICML 2019 Vol. 2019-June (pp. 6916-6936). Long Beach, California, USA: Curran Associates, Inc. Scopus16 |
| 2019 | Mitrovic, J., Sejdinovic, D., & Teh, Y. W. (2019). Causal inference via kernel deviance measures. In Advances in Neural Information Processing Systems Vol. 2018-December (pp. 6986-6994). Montreal, Canada: Curran Associates, Inc. Scopus32 WoS15 |
| 2019 | Li, Z., Ton, J. F., Oglic, D., & Sejdinovic, D. (2019). Towards a Unified Analysis of Random Fourier Features. In Proceedings of Machine Learning Research Vol. 97 (pp. 3905-3914). Scopus63 |
| 2018 | Law, H. C. L., Sutherland, D. J., Sejdinovic, D., & Flaxman, S. (2018). Bayesian Approaches to Distribution Regression. In Proceedings of Machine Learning Research Vol. 84. |
| 2018 | Leon Law, H. C., Sejdinovic, D., Cameron, E., Lucas, T. C. D., Flaxman, S., Battle, K., & Fukumizu, K. (2018). Variational learning on aggregate outputs with Gaussian processes. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems Vol. 31 (pp. 6081-6091). San Francisco. CA., USA.: Association for Computing Machinery. Scopus18 WoS7 |
| 2018 | Caterini, A. L., Doucet, A., Sejdinovic, D., Bengio, S., Wallach, H., Larochelle, H., . . . Garnett, R. (2018). Hamiltonian variational auto-encoder. In Advances in Neural Information Processing Systems Vol. 2018-December (pp. 8167-8177). Cambridge, Massachusetts, USA: MIT Press. Scopus54 WoS18 |
| 2018 | Law, H. C. L., Sutherland, D. J., Sejdinovic, D., & Flaxman, S. (2018). Bayesian approaches to distribution regression. In S. Fernando, & F. Perez-Cruz (Eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics Vol. 84 (pp. 1167-1176). Lanzarote, Canary Islands: PMLR. Scopus16 WoS5 |
| 2017 | Law, H. C. L., Yau, C., & Sejdinovic, D. (2017). Testing and learning on distributions with symmetric noise invariance. In Advances in Neural Information Processing Systems Vol. 2017-December (pp. 1344-1354). Long Beach, CA, USA: Curran Associattes. Scopus6 |
| 2017 | Mitrovic, J., Sejdinovic, D., & Teh, Y. W. (2017). Deep kernel machines via the kernel reparametrization trick. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings (pp. 4 pages). Toulon, France: Open Review. Scopus1 |
| 2017 | Schuster, I., Strathmann, H., Paige, B., & Sejdinovic, D. (2017). Kernel Sequential Monte Carlo. In Machine learning and Knowledge Discovery in Databases Vol. 10534 (pp. 390-409). Skopje, Macedonia: Springer International Publishing. DOI Scopus4 WoS5 |
| 2017 | Zhang, Q., Filippi, S., Flaxman, S., & Sejdinovic, D. (2017). Feature-to-feature regression for a two-step conditional independence test. In Uncertainty in Artificial Intelligence - Proceedings of the 33rd Conference, UAI 2017 (pp. 10 pages). Sydney, Aust: Proceedings of Machine Learning Research (PMLR). Scopus15 WoS7 |
| 2017 | Flaxman, S., Teh, Y. W., & Sejdinovic, D. (2017). Poisson intensity estimation with reproducing kernels. In A. Singh, & J. Zhu (Eds.), ARTIFICIAL INTELLIGENCE AND STATISTICS Vol. 54 (pp. 270-279). Fort Lauderdale, FL: MICROTOME PUBLISHING. Scopus25 WoS14 |
| 2016 | Park, M., Jitkrittum, W., & Sejdinovic, D. (2016). K2-ABC: Approximate bayesian computation with kernel embeddings. In A. Gretton, & C. C. Robert (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016 Vol. 51 (pp. 398-407). Cadiz, Spain: MLR Press. Scopus68 WoS55 |
| 2016 | Franchi, G., Angulo, J., & Sejdinovic, D. (2016). Hyperspectral image classification with support vector machines on kernel distribution embeddings. In Proceedings International Conference on Image Processing Icip Vol. 2016-August (pp. 1898-1902). AZ, Phoenix: IEEE. DOI Scopus7 WoS7 |
| 2016 | Paige, B., Sejdinovic, D., & Wood, F. (2016). Super-sampling with a reservoir. In 32nd Conference on Uncertainty in Artificial Intelligence 2016 Uai 2016 (pp. 567-576). Scopus7 |
| 2016 | Flaxman, S., Sejdinovic, D., Cunningham, J. P., & Filippi, S. (2016). Bayesian learning of kernel embeddings. In 32nd Conference on Uncertainty in Artificial Intelligence 2016 Uai 2016 (pp. 182-191). Scopus24 |
| 2016 | Mitrovic, J., Sejdinovic, D., & Teh, Y. W. (2016). DR-ABC: Approximate Bayesian computation with kernel-based distribution regression. In Proceedings of Machine Learning Research Vol. 48 (pp. 2209-2218). New York City, NY, USA: MLR Press. Scopus13 WoS6 |
| 2016 | Vukobratovic, D., Jakovetic, D., Skachek, V., Bajovic, D., & Sejdinovic, D. (2016). Network function computation as a service in future 5G machine type communications. In 2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC) Vol. 2016-October (pp. 365-369). Brest, France: IEEE. DOI Scopus3 |
| 2015 | Jitkrittum, W., Gretton, A., Heess, N., Eslami, S. M. A., Lakshminarayanan, B., Sejdinovic, D., & Szabó, Z. (2015). Kernel-based just-in-time learning for passing expectation propagation messages. In M. Meila, & T. Heskes (Eds.), Uncertainty in Artificial Intelligence Proceedings of the 31st Conference Uai 2015 (pp. 405-414). NETHERLANDS, Amsterdam: AUAI PRESS. Scopus18 WoS9 |
| 2015 | Chwialkowski, K., Ramdas, A., Sejdinovic, D., & Gretton, A. (2015). Fast two-sample testing with analytic representations of probability measures. In Advances in Neural Information Processing Systems Vol. 2015-January (pp. 1981-1989). Scopus114 |
| 2015 | Strathmann, H., Sejdinovic, D., Livingstone, S., Szabo, Z., & Gretton, A. (2015). Gradient-free Hamiltonian Monte Carlo with efficient Kernel exponential families. In Advances in Neural Information Processing Systems Vol. 2015-January (pp. 955-963). Scopus51 |
| 2015 | Vukobratovic, D., Sejdinovic, D., & Pizurica, A. (2015). Compressed Sensing using sparse binary measurements: A rateless coding perspective. In IEEE Workshop on Signal Processing Advances in Wireless Communications Spawc Vol. 2015-August (pp. 86-90). Stockholm, SWEDEN: IEEE. DOI |
| 2015 | Chwialkowski, K., Ramdas, A., Sejdinovic, D., & Gretton, A. (2015). Fast Two-Sample Testing with Analytic Representations of Probability Measures. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015) Vol. 28 (pp. 9 pages). CANADA, Montreal: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). WoS45 |
| 2014 | Chwialkowski, K., Sejdinovic, D., & Gretton, A. (2014). A wild bootstrap for degenerate kernel tests. In Advances in Neural Information Processing Systems Vol. 4 (pp. 3608-3616). Scopus46 |
| 2014 | Sejdinovic, D., Strathmann, H., Garcia, M. L., Andrieu, C., & Gretton, A. (2014). Kernel Adaptive Metropolis-Hastings. In 31st International Conference on Machine Learning, ICML 2014 Vol. 32 (pp. 1665-1673). Bejing, PEOPLES R CHINA: JMLR-JOURNAL MACHINE LEARNING RESEARCH. Scopus12 WoS18 |
| 2013 | Sejdinovic, D., Gretton, A., & Bergsma, W. (2013). A kernel test for three-variable interactions. In Advances in Neural Information Processing Systems. Scopus30 |
| 2012 | Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., & Fukumizu, K. (2012). Optimal kernel choice for large-scale two-sample tests. In Advances in Neural Information Processing Systems Vol. 2 (pp. 1205-1213). Scopus508 |
| 2012 | Piechocki, R. J., & Sejdinovic, D. (2012). Combinatorial channel signature modulation for wireless ad-hoc networks. In IEEE International Conference on Communications (pp. 4684-4689). IEEE. DOI Scopus1 |
| 2012 | Müller, A., Sejdinovic, D., & Piechocki, R. (2012). Approximate message passing under finite alphabet constraints. In ICASSP IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (pp. 3177-3180). Kyoto, JAPAN: IEEE. DOI Scopus3 WoS3 |
| 2012 | Sejdinovic, D., Gretton, A., Sriperumbudur, B., & Fukumizu, K. (2012). Hypothesis testing using pairwise distances and associated kernels. In Proceedings of the 29th International Conference on Machine Learning Icml 2012 Vol. 2 (pp. 1111-1118). Scopus19 |
| 2010 | Sejdinovic, D., & Johnson, O. (2010). Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction. In 2010 48th Annual Allerton Conference on Communication Control and Computing Allerton 2010 (pp. 998-1003). IEEE. DOI Scopus73 |
| 2010 | Sejdinović, D., Andrieu, C., & Piechocki, R. (2010). Bayesian sequential compressed sensing in sparse dynamical systems. In 2010 48th Annual Allerton Conference on Communication Control and Computing Allerton 2010 (pp. 1730-1736). IEEE. DOI Scopus28 |
| 2009 | Sejdinović, D., Piechocki, R. J., & Doufexi, A. (2009). AND-OR tree analysis of distributed LT codes. In Proceedings 2009 IEEE Information Theory Workshop on Networking and Information Theory Itw 2009 (pp. 261-265). Volos, GREECE: IEEE. DOI Scopus62 WoS39 |
| 2009 | Vukobratovic, D., Stankovic, V., Stankovic, L., & Sejdinovic, D. (2009). Precoded EWF codes for unequal error protection of scalable video. In Proceedings of the 5th International Conference on Mobile Multimedia Communications Mobimedia 2009. ICST. DOI Scopus6 |
| 2009 | Sejdinović, D., Piechocki, R. J., & Doufexi, A. (2009). Rateless distributed source code design. In Proceedings of the 5th International Conference on Mobile Multimedia Communications Mobimedia 2009. ICST. DOI Scopus3 |
| 2008 | Sejdinović, D., Piechocki, R. J., Doufexi, A., & Ismail, M. (2008). Rate adaptive binary erasure quantization with dual fountain codes. In Globecom IEEE Global Telecommunications Conference (pp. 1203-1207). New Orleans, LA: IEEE. DOI Scopus1 WoS1 |
| 2008 | Sejdinović, D., Ponnampalam, V., Piechocki, R. J., & Doufexi, A. (2008). The throughput analysis of different IR-HARQ schemes based on fountain codes. In IEEE Wireless Communications and Networking Conference Wcnc (pp. 267-272). Las Vegas, NV: IEEE. DOI Scopus5 WoS3 |
| 2008 | Sejdinović, D., Piechocki, R. J., Doufexi, A., & Ismail, M. (2008). Fountain coding with decoder side information. In IEEE International Conference on Communications (pp. 4477-4482). Beijing, PEOPLES R CHINA: IEEE. DOI Scopus4 |
| 2008 | Vukobratovic, D., Stankovic, V., Sejdinovic, D., Stankovic, L., & Xiong, Z. (2008). Expanding Window Fountain codes for scalable video multicast. In 2008 IEEE International Conference on Multimedia and Expo Icme 2008 Proceedings (pp. 77-80). Hannover, GERMANY: IEEE. DOI Scopus19 WoS6 |
| 2007 | Sejdinović, D., Vukobratović, D., Doufexi, A., Šenk, V., & Piechocki, R. J. (2007). Expanding Window Fountain codes for unequal error protection. In M. B. Matthews (Ed.), Conference Record Asilomar Conference on Signals Systems and Computers (pp. 1020-1024). Pacific Grove, CA: IEEE. DOI Scopus34 WoS13 |
| 2007 | Vukobratović, D., Stanković, V., Sejdinović, D., Fagoonee, L., & Xiong, Z. (2007). Scalable bata multicast using expanding window fountain codes. In 45th Annual Allerton Conference on Communication Control and Computing 2007 Vol. 1 (pp. 344-351). Scopus7 |
| Year | Citation |
|---|---|
| 2025 | Wild, V., Wu, J., Sejdinovic, D., & Knoblauch, J. (2025). Near-Optimal Approximations for Bayesian Inference in Function Space. |
| 2024 | Sejdinovic, D. (2024). An Overview of Causal Inference using Kernel Embeddings. |
| 2022 | Martinez-Taboada, D., & Sejdinovic, D. (2022). Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings. |
| 2022 | Martinez-Taboada, D., & Sejdinovic, D. (2022). Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation. |
| 2021 | Zhu, H., Howes, A., Eer, O. V., Rischard, M., Li, Y., Sejdinovic, D., & Flaxman, S. (2021). Aggregated Gaussian Processes with Multiresolution Earth Observation Covariates. |
| 2021 | Wild, V., Kanagawa, M., & Sejdinovic, D. (2021). Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. |
| 2018 | Kanagawa, M., Hennig, P., Sejdinovic, D., & Sriperumbudur, B. K. (2018). Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. |
| 2015 | Strathmann, H., Sejdinovic, D., & Girolami, M. (2015). Unbiased Bayes for Big Data: Paths of Partial Posteriors. |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2026 | Principal Supervisor | Likelihood-free deep learning methods for phylogenetics | Master of Research | Master | Full Time | Mr Alexander Tarin Smith |
| 2026 | Principal Supervisor | Connecting Sparse Variational Methods in Gaussian Processes and Kernel Methods | Master of Research | Master | Full Time | Mr Kyan Marcel Percevault |
| 2025 | Principal Supervisor | Function Space Variational Deep Learning | Master of Philosophy | Master | Full Time | Mr Rahul Tejeshwa |
| 2025 | Co-Supervisor | Causal learning for optimising industry relevant traits in Australian grain production systems | Doctor of Philosophy | Doctorate | Full Time | Mr Aidan James Moller |
| 2025 | Principal Supervisor | Function Space Variational Deep Learning | Master of Philosophy | Master | Full Time | Mr Rahul Tejeshwa |
| 2025 | Co-Supervisor | Causal learning for optimising industry relevant traits in Australian grain production systems | Doctor of Philosophy | Doctorate | Full Time | Mr Aidan James Moller |
| 2024 | Principal Supervisor | Uncertainty Calibration of Probabilistic Machine Learning Models | Master of Philosophy | Master | Full Time | Mr Peter Moskvichev |
| 2024 | Principal Supervisor | Multi-Agent Machine Learning for Object-Tracking | Doctor of Philosophy | Doctorate | Full Time | Mr Vinh Thanh Nguyen |
| 2024 | Principal Supervisor | Uncertainty Calibration of Probabilistic Machine Learning Models | Master of Philosophy | Master | Full Time | Mr Peter Moskvichev |
| 2024 | Principal Supervisor | Multi-Agent Machine Learning for Object-Tracking | Doctor of Philosophy | Doctorate | Full Time | Mr Vinh Thanh Nguyen |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 2023 - 2025 | Principal Supervisor | Geolocation with Latent Variable Models | Master of Philosophy | Master | Full Time | Miss Vivienne Mei-Larn Niejalke |
| Date | Role | Research Topic | Location | Program | Supervision Type | Student Load | Student Name |
|---|---|---|---|---|---|---|---|
| 2020 - 2025 | Principal Supervisor | Data quality in causal machine learning with applications to algorithmic fairness | University of Oxford | DPhil | Doctorate | Full Time | Jake Fawkes |
| 2020 - 2025 | Principal Supervisor | Generalised variational inference in infinite dimensions | University of Oxford | DPhil | Doctorate | Full Time | Veit David Wild |
| 2020 - 2024 | Principal Supervisor | Transforming kernel-based learners to incorporate domain knowledge from climate science | University of Oxford | DPhil | Doctorate | Full Time | Shahine Bouabid |
| 2019 - 2024 | Principal Supervisor | Quantifying and mitigating selection bias in probability and nonprobability samples | University of Oxford | DPhil | Doctorate | Full Time | Valerie C. Bradley |
| 2019 - 2023 | Principal Supervisor | Towards Trustworthy Machine Learning with Kernels | University of Oxford | DPhil | Doctorate | Full Time | Siu Lun Chau |
