Research Interests
Machine learning Statistical data science Computational statistics Artificial IntelligenceProfessor Dino Sejdinovic
Professor
School of Mathematical Sciences
College of Science
Eligible to supervise Masters and PhD - email supervisor to discuss availability.
Dino Sejdinovic is a Professor at the School of Computer and Mathematical Sciences, University of Adelaide. 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 | University of Adelaide |
| 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 | 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. Scopus3 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. |
| 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 | 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). Scopus7 |
| 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. Scopus6 WoS7 |
| 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 | 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 | Li, Z., Su, W. J., & Sejdinovic, D. (2023). Benign Overfitting and Noisy Features. Journal of the American Statistical Association, 118(544), 2876-2888. Scopus6 WoS6 |
| 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. Scopus2 WoS3 |
| 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. Scopus19 |
| 2022 | Bouabid, S., Watson-Parris, D., Stefanović, S., Nenes, A., & Sejdinovic, D. (2022). AODisaggregation: toward global aerosol vertical profiles. |
| 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. Scopus5 WoS2 |
| 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 WoS12 |
| 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 WoS11 |
| 2022 | Fawkes, J., Evans, R. J., & Sejdinovic, D. (2022). Selection, Ignorability and Challenges with Causal Fairness. Proceedings of Machine Learning Research, 177, 275-289. Scopus6 |
| 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 |
| 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. |
| 2022 | Matabuena, M., Vidal, J. C., Padilla, O. H. M., & Sejdinovic, D. (2022). Kernel Biclustering algorithm in Hilbert Spaces. |
| 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 | 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 | Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., . . . Ares, N. (2021). Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning. |
| 2021 | Wild, V., Kanagawa, M., & Sejdinovic, D. (2021). Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes. |
| 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. Scopus6 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. Scopus177 WoS162 Europe PMC113 |
| 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. Scopus31 WoS31 |
| 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. Scopus48 WoS32 |
| 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 WoS18 |
| 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. Scopus19 WoS17 |
| 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. Scopus62 WoS61 Europe PMC11 |
| 2020 | Rindt, D., Sejdinovic, D., & Steinsaltz, D. (2020). Consistency of permutation tests for HSIC and dHSIC. |
| 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. Scopus49 WoS46 Europe PMC16 |
| 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. Scopus114 WoS102 |
| 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. Scopus711 WoS612 Europe PMC148 |
| 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. Scopus42 WoS36 Europe PMC9 |
| 2018 | Zhang, Q., Filippi, S., Gretton, A., & Sejdinovic, D. (2018). Large-scale kernel methods for independence testing. Statistics and Computing, 28(1), 113-130. Scopus84 WoS67 |
| 2018 | Kanagawa, M., Hennig, P., Sejdinovic, D., & Sriperumbudur, B. K. (2018). Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. |
| 2017 | Flaxman, S., Teh, Y. W., & Sejdinovic, D. (2017). Poisson intensity estimation with reproducing kernels. Electronic Journal of Statistics, 11(2), 5081-5104. Scopus17 WoS13 |
| 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. Scopus50 WoS46 Europe PMC52 |
| 2015 | Strathmann, H., Sejdinovic, D., & Girolami, M. (2015). Unbiased Bayes for Big Data: Paths of Partial Posteriors. |
| 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. Scopus524 WoS440 |
| 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 (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 | Moskvichev, P., & Sejdinovic, D. (2025). All Models Are Miscalibrated, But Some Less So: Comparing Calibration with Conditional Mean Operators. |
| 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 Scopus1 |
| 2025 | Chau, S. L., Schrab, A., Gretton, A., Sejdinovic, D., & Muandet, K. (2025). Credal Two-Sample Tests of Epistemic Uncertainty. In Proceedings of Machine Learning Research Vol. 258 (pp. 127-135). Scopus1 |
| 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 Scopus2 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). Scopus6 |
| 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. Scopus9 |
| 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). WoS3 |
| 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. Scopus28 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). Scopus12 |
| 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). Scopus9 |
| 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 Scopus13 WoS8 |
| 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. Scopus9 |
| 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. Scopus3 |
| 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 WoS2 |
| 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). Scopus62 |
| 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 | 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. Scopus52 WoS18 |
| 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 | 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. Scopus24 WoS13 |
| 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 WoS54 |
| 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). Scopus23 |
| 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 WoS8 |
| 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). Scopus109 |
| 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). WoS43 |
| 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 WoS17 |
| 2013 | Sejdinovic, D., Gretton, A., & Bergsma, W. (2013). A kernel test for three-variable interactions. In Advances in Neural Information Processing Systems. Scopus29 |
| 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). Scopus502 |
| 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). Scopus18 |
| 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 Scopus27 |
| 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 WoS2 |
| 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. |
| 2024 | Fawkes, J., Hu, R., Evans, R. J., & Sejdinovic, D. (2024). Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects. Scopus1 |
| 2024 | Doan, B. G., Shamsi, A., Guo, X. -Y., Mohammadi, A., Alinejad-Rokny, H., Sejdinovic, D., . . . Abbasnejad, E. (2024). Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks. |
| 2021 | Hu, R., Sejdinovic, D., & Evans, R. J. (2021). A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment. |
| Date | Role | Research Topic | Program | Degree Type | Student Load | Student Name |
|---|---|---|---|---|---|---|
| 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 |
| 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 - ongoing | Principal Supervisor | Uncertainty quantification in large scale machine learning | 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 |
| 2020 - ongoing | Co-Supervisor | On the Achievability of Causal Fairness | University of Oxford | DPhil | Doctorate | Full Time | Jake Fawkes |
| 2019 - 2024 | Principal Supervisor | Quantifying and mitigating selection bias in probability and nonprobability samples | University of Oxford | DPhil | Doctorate | Full Time | Valerie C. Bradley |
