
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.
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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
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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.
Scopus1 WoS12022 Li, Z., Su, W. J., & Sejdinovic, D. (2022). Benign Overfitting and Noisy Features. Journal of the American Statistical Association, 13 pages.
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.
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.
WoS12022 Hu, R., Nicholls, G. K., & Sejdinovic, D. (2022). Large scale tensor regression using kernels and variational inference. Machine Learning, 111(7), 2663-2713.
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.
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.
Scopus36 Europe PMC262021 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.
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.
Scopus11 WoS102021 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 WoS42021 Rindt, D., Sejdinovic, D., & Steinsaltz, D. (2021). Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC. Stat, 10(1), 10 pages.
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.
Scopus4 WoS72021 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.
Scopus32021 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.
Scopus12020 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.
Scopus7 WoS72020 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.
Scopus21 WoS21 Europe PMC12019 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.
Scopus33 WoS30 Europe PMC92019 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.
Scopus55 WoS512019 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.
Scopus188 WoS175 Europe PMC222019 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.
Scopus22018 Ton, J. F., Flaxman, S., Sejdinovic, D., & Bhatt, S. (2018). Spatial mapping with Gaussian processes and nonstationary Fourier features. Spatial Statistics, 28, 59-78.
Scopus27 WoS21 Europe PMC42018 Zhang, Q., Filippi, S., Gretton, A., & Sejdinovic, D. (2018). Large-scale kernel methods for independence testing. Statistics and Computing, 28(1), 113-130.
Scopus38 WoS562017 Flaxman, S., Teh, Y. W., & Sejdinovic, D. (2017). Poisson intensity estimation with reproducing kernels. Electronic Journal of Statistics, 11(2), 5081-5104.
Scopus11 WoS92016 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.
Scopus26 WoS162015 Kurth-Nelson, Z., Barnes, G., Sejdinovic, D., Dolan, R., & Dayan, P. (2015). Temporal structure in associative retrieval. eLife, 2015(4), 18 pages.
Scopus38 WoS38 Europe PMC192014 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 WoS12013 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.
Scopus285 WoS2582010 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 WoS162009 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 WoS182009 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.
Scopus148 WoS1202009 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.
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).
Scopus12020 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).
Scopus12020 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.
Scopus2 WoS12019 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 WoS22019 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.
Scopus42019 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 WoS82018 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 WoS12018 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.
Scopus102018 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 WoS132017 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.
WoS72017 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).
Scopus32017 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.
Scopus12017 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.
Scopus3 WoS32017 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 WoS42016 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.
Scopus5 WoS52016 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).
Scopus22016 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).
Scopus132016 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 WoS142016 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.
Scopus32016 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 WoS272015 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 WoS62015 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).
Scopus582015 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).
Scopus382015 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.
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).
Scopus202014 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 WoS112013 Sejdinovic, D., Gretton, A., & Bergsma, W. (2013). A kernel test for three-variable interactions. In Advances in Neural Information Processing Systems.
Scopus172012 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).
Scopus3132012 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.
Scopus12012 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.
Scopus3 WoS32012 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).
Scopus162010 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.
Scopus592010 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.
Scopus252009 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.
Scopus61 WoS392009 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.
Scopus52009 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.
Scopus32008 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.
Scopus1 WoS12008 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.
Scopus4 WoS32008 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.
Scopus42008 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.
Scopus19 WoS62007 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.
Scopus33 WoS132007 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).
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