Dino Sejdinovic

Professor Dino Sejdinovic

Professor

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

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.

  • Appointments
    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

  • Education
    Date Institution name Country Title
    2009 University of Bristol United Kingdom PhD
    2006 University of Sarajevo Bosnia and Herzegovina Dipl.Math.-Inf.

  • Research Interests
    Knowledge Representation and Machine Learning Statistics

  • Journals
    Year Citation
    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.
    DOI Scopus1 WoS1
    2022 Li, Z., Su, W. J., & Sejdinovic, D. (2022). Benign Overfitting and Noisy Features. Journal of the American Statistical Association, 13 pages.
    DOI
    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, 11 pages.
    DOI
    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.
    DOI WoS1
    2022 Hu, R., Nicholls, G. K., & Sejdinovic, D. (2022). Large scale tensor regression using kernels and variational inference. Machine Learning, 111(7), 2663-2713.
    DOI
    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.
    DOI
    2022 Rindt, D., Hu, R., Steinsaltz, D., & Sejdinovic, D. (2022). Survival Regression with Proper Scoring Rules and Monotonic Neural Networks. INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151, 151, 16 pages.
    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.
    2021 Ton, J. -F., Chan, L., Teh, Y. W., & Sejdinovic, D. (2021). Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings. 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS), 130, 11 pages.
    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.
    DOI Scopus36 Europe PMC26
    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, 1-12.
    DOI
    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.
    DOI Scopus11 WoS10
    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, 51 pages.
    Scopus6 WoS4
    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.
    DOI
    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.
    DOI Scopus4 WoS7
    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.
    DOI Scopus3
    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.
    DOI Scopus1
    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.
    DOI Scopus7 WoS7
    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.
    DOI Scopus21 WoS21 Europe PMC1
    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.
    DOI Scopus33 WoS30 Europe PMC9
    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.
    DOI Scopus55 WoS51
    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.
    DOI Scopus188 WoS175 Europe PMC22
    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.
    DOI Scopus2
    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.
    DOI Scopus27 WoS21 Europe PMC4
    2018 Zhang, Q., Filippi, S., Gretton, A., & Sejdinovic, D. (2018). Large-scale kernel methods for independence testing. Statistics and Computing, 28(1), 113-130.
    DOI Scopus38 WoS56
    2017 Flaxman, S., Teh, Y. W., & Sejdinovic, D. (2017). Poisson intensity estimation with reproducing kernels. Electronic Journal of Statistics, 11(2), 5081-5104.
    DOI Scopus11 WoS9
    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.
    DOI Scopus26 WoS16
    2015 Kurth-Nelson, Z., Barnes, G., Sejdinovic, D., Dolan, R., & Dayan, P. (2015). Temporal structure in associative retrieval. eLife, 2015(4), 18 pages.
    DOI Scopus38 WoS38 Europe PMC19
    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.
    DOI 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.
    DOI Scopus285 WoS258
    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.
    DOI Scopus18 WoS16
    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.
    DOI 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.
    DOI Scopus148 WoS120
    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.
    DOI Scopus121 WoS104
    - Schuff, J., Lennon, D. T., Geyer, S., Craig, D. L., Fedele, F., Vigneau, F., . . . Ares, N. (n.d.). Identifying Pauli spin blockade using deep learning.
    - Wild, V. D., Hu, R., & Sejdinovic, D. (n.d.). Generalized Variational Inference in Function Spaces: Gaussian Measures
    meet Bayesian Deep Learning.
    - Hu, R., Chau, S. L., Sejdinovic, D., & Glaunès, J. A. (n.d.). Giga-scale Kernel Matrix Vector Multiplication on GPU.
    - Rindt, D., Sejdinovic, D., & Steinsaltz, D. (n.d.). Consistency of permutation tests for HSIC and dHSIC.
    - Sejdinovic, D. (n.d.). Discussion of "Functional Models for Time-Varying Random Objects'' by
    Dubey and Müller.
    - Watson-Parris, D., Sutherland, S., Christensen, M., Caterini, A., Sejdinovic, D., & Stier, P. (n.d.). Detecting anthropogenic cloud perturbations with deep learning.
    - Kanagawa, M., Hennig, P., Sejdinovic, D., & Sriperumbudur, B. K. (n.d.). Gaussian Processes and Kernel Methods: A Review on Connections and
    Equivalences.
    - Strathmann, H., Sejdinovic, D., & Girolami, M. (n.d.). Unbiased Bayes for Big Data: Paths of Partial Posteriors.
    - Fawkes, J., Hu, R., Evans, R. J., & Sejdinovic, D. (n.d.). Doubly Robust Kernel Statistics for Testing Distributional Treatment
    Effects Even Under One Sided Overlap.
    - Martinez-Taboada, D., & Sejdinovic, D. (n.d.). Sequential Decision Making on Unmatched Data using Bayesian Kernel
    Embeddings.
    - Martinez-Taboada, D., & Sejdinovic, D. (n.d.). Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation.
    - Matabuena, M., Vidal, J. C., Padilla, O. H. M., & Sejdinovic, D. (n.d.). Kernel Biclustering algorithm in Hilbert Spaces.
    - Zhu, H., Howes, A., Eer, O. V., Rischard, M., Li, Y., Sejdinovic, D., & Flaxman, S. (n.d.). Aggregated Gaussian Processes with Multiresolution Earth Observation
    Covariates.
    - Craig, D. L., Moon, H., Fedele, F., Lennon, D. T., Straaten, B. V., Vigneau, F., . . . Ares, N. (n.d.). Bridging the reality gap in quantum devices with physics-aware machine
    learning.
    - Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., . . . Ares, N. (n.d.). Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices
    Using Machine Learning.
    - Wild, V., Kanagawa, M., & Sejdinovic, D. (n.d.). Connections and Equivalences between the Nyström Method and Sparse
    Variational Gaussian Processes.
    - Hu, R., Chau, S. L., Huertas, J. F., & Sejdinovic, D. (n.d.). Explaining Preferences with Shapley Values.
    - Chau, S. L., Hu, R., Gonzalez, J., & Sejdinovic, D. (n.d.). RKHS-SHAP: Shapley Values for Kernel Methods.
    - Bouabid, S., Watson-Parris, D., Stefanović, S., Nenes, A., & Sejdinovic, D. (n.d.). AODisaggregation: toward global aerosol vertical profiles.
    - Chau, S. L., Cucuringu, M., & Sejdinovic, D. (n.d.). Spectral Ranking with Covariates.
    - Fawkes, J., Evans, R., & Sejdinovic, D. (n.d.). Selection, Ignorability and Challenges With Causal Fairness.
    - Hu, R., Sejdinovic, D., & Evans, R. J. (n.d.). A Kernel Test for Causal Association via Noise Contrastive Backdoor
    Adjustment.

  • Conference Papers
    Year Citation
    2021 Chau, S. L., Bouabid, S., & Sejdinovic, D. (2021). Deconditional Downscaling with Gaussian Processes. In Advances in Neural Information Processing Systems Vol. 22 (pp. 17813-17825).
    2021 Chau, S. L., Ton, J. F., González, J., Teh, Y. W., & Sejdinovic, D. (2021). BAYESIMP: Uncertainty Quantification for Causal Data Fusion. In Advances in Neural Information Processing Systems Vol. 5 (pp. 3466-3477).
    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). ELECTR NETWORK: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE.
    2021 Caterini, A., Cornish, R., Sejdinovic, D., & Doucet, A. (2021). Variational Inference with Continuously-Indexed Normalizing Flows. In 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021 (pp. 44-53).
    Scopus1
    2020 Rudner, T. G. J., Sejdinovic, D., & Gal, Y. (2020). Inter-domain deep gaussian processes. In 37th International Conference on Machine Learning, ICML 2020 Vol. PartF168147-11 (pp. 8256-8264).
    Scopus1
    2020 Raj, A., Law, H. C. L., Sejdinovic, D., & Park, M. (2020). A Differentially Private Kernel Two-Sample Test. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11906 LNAI (pp. 697-724). Wurzburg, GERMANY: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus2 WoS1
    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 Vol. 32 (pp. 12 pages). Vancouver, CANADA: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
    Scopus6 WoS2
    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.
    Scopus4
    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.
    Scopus12 WoS8
    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. CesaBianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems Vol. 2018-December (pp. 6081-6091). Montreal, CANADA: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
    Scopus10 WoS1
    2018 Law, H. C. L., Sutherland, D. J., Sejdinovic, D., & Flaxman, S. (2018). Bayesian approaches to distribution regression. In A. Storkey, & F. PerezCruz (Eds.), International Conference on Artificial Intelligence and Statistics, AISTATS 2018 Vol. 84 (pp. 1167-1176). Lanzarote, SPAIN: MICROTOME PUBLISHING.
    Scopus10
    2018 Caterini, A. L., Doucet, A., & Sejdinovic, D. (2018). Hamiltonian variational auto-encoder. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. CesaBianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems Vol. 2018-December (pp. 8167-8177). Montreal, CANADA: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
    Scopus15 WoS13
    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 Vol. 54 (pp. 270-279). Fort Lauderdale, FL: MICROTOME PUBLISHING.
    WoS7
    2017 Law, H. C. L., Yau, C., & Sejdinovic, D. (2017). Testing and learning on distributions with symmetric noise invariance. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Advances in Neural Information Processing Systems Vol. 2017-December (pp. 1344-1354). Long Beach, CA: NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
    Scopus3
    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 M. Ceci, J. Hollmen, L. Todorovski, C. Vens, & S. Dzeroski (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10534 LNAI (pp. 390-409). Skopje, MACEDONIA: SPRINGER INTERNATIONAL PUBLISHING AG.
    DOI Scopus3 WoS3
    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, AUSTRALIA: AUAI PRESS.
    Scopus7 WoS4
    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). Phoenix, AZ: IEEE.
    DOI Scopus5 WoS5
    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).
    Scopus2
    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).
    Scopus13
    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.
    Scopus7 WoS14
    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
    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.
    Scopus26 WoS27
    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). Amsterdam, NETHERLANDS: AUAI PRESS.
    Scopus13 WoS6
    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).
    Scopus58
    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).
    Scopus38
    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
    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).
    Scopus20
    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.
    Scopus8 WoS11
    2013 Sejdinovic, D., Gretton, A., & Bergsma, W. (2013). A kernel test for three-variable interactions. In Advances in Neural Information Processing Systems.
    Scopus17
    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).
    Scopus313
    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).
    Scopus16
    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 Scopus59
    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 Scopus25
    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 Scopus61 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 Scopus5
    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 Scopus4 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 Scopus33 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
  • Position: Professor
  • Phone: 83133797
  • Email: dino.sejdinovic@adelaide.edu.au
  • Campus: North Terrace
  • Building: Ingkarni Wardli, floor Level Six
  • Org Unit: Mathematical Sciences
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