Dino Sejdinovic

Dino Sejdinovic

Statistical machine learning, kernel methods, nonparametric statistics

I am an Associate Professor in Statistics at the University of Oxford, a Fellow of Mansfield College, and a Faculty Fellow of the Alan Turing Institute. I conduct research at the interface between machine learning and statistical methodology, with an emphasis on nonparametric and kernel methods.

Publications

2017

  • H. Law, D. Sutherland, D. Sejdinovic, S. Flaxman, Bayesian Distribution Regression, ArXiv e-prints:1705.04293, 2017.
  • Q. Zhang, S. Filippi, S. Flaxman, D. Sejdinovic, Feature-to-Feature Regression for a Two-Step Conditional Independence Test, in Uncertainty in Artificial Intelligence (UAI), 2017.
    Project: bigbayes
  • J. Mitrovic, D. Sejdinovic, Y. W. Teh, Deep Kernel Machines via the Kernel Reparametrization Trick, in International Conference on Learning Representations (ICLR) Workshop Track, 2017.
  • H. Law, C. Yau, D. Sejdinovic, Testing and Learning on Distributions with Symmetric Noise Invariance, in Advances in Neural Information Processing Systems (NIPS), 2017.
  • J. Runge, D. Sejdinovic, S. Flaxman, Detecting causal associations in large nonlinear time series datasets, ArXiv e-prints:1702.07007, 2017.
  • I. Schuster, H. Strathmann, B. Paige, D. Sejdinovic, Kernel Sequential Monte Carlo, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD). 2017.
  • S. Flaxman, Y. W. Teh, D. Sejdinovic, Poisson Intensity Estimation with Reproducing Kernels, in Artificial Intelligence and Statistics (AISTATS), 2017.
    Project: bigbayes
  • Q. Zhang, S. Filippi, A. Gretton, D. Sejdinovic, Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, to appear, 2017.
    Project: bigbayes

2016

  • D. Vukobratovic, D. Jakovetic, V. Skachek, D. Bajovic, D. Sejdinovic, G. Karabulut Kurt, C. Hollanti, I. Fischer, CONDENSE: A Reconfigurable Knowledge Acquisition Architecture for Future 5G IoT, IEEE Access, vol. 4, 3360–3378, 2016.
  • D. Vukobratovic, D. Jakovetic, V. Skachek, D. Bajovic, D. Sejdinovic, Network Function Computation as a Service in Future 5G Machine Type Communications, in International Symposium on Turbo Codes & Iterative Information Processing (ISTC), 2016, 365–369.
  • J. Mitrovic, D. Sejdinovic, Y. W. Teh, DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression, in International Conference on Machine Learning (ICML), 2016, 1482–1491.
    Project: bigbayes
  • G. Franchi, J. Angulo, D. Sejdinovic, Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings, in IEEE International Conference on Image Processing (ICIP), 2016, 1898–1902.
  • B. Paige, D. Sejdinovic, F. Wood, Super-Sampling with a Reservoir, in Uncertainty in Artificial Intelligence (UAI), 2016, 567–576.
  • S. Flaxman, D. Sejdinovic, J. Cunningham, S. Filippi, Bayesian Learning of Kernel Embeddings, in Uncertainty in Artificial Intelligence (UAI), 2016, 182–191.
    Project: bigbayes
  • M. Park, W. Jitkrittum, D. Sejdinovic, K2-ABC: Approximate Bayesian Computation with Kernel Embeddings, in Artificial Intelligence and Statistics (AISTATS), 2016, 398–407.

2015

  • F. Briol, C. Oates, M. Girolami, M. Osborne, D. Sejdinovic, Probabilistic Integration: A Role for Statisticians in Numerical Analysis?, ArXiv e-prints:1512.00933, 2015.
  • H. Strathmann, D. Sejdinovic, M. Girolami, Unbiased Bayes for Big Data: Paths of Partial Posteriors, ArXiv e-prints:1501.03326, 2015.
  • H. Strathmann, D. Sejdinovic, S. Livingstone, Z. Szabo, A. Gretton, Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families, in Advances in Neural Information Processing Systems (NIPS), vol. 28, 2015, 955–963.
  • K. Chwialkowski, A. Ramdas, D. Sejdinovic, A. Gretton, Fast Two-Sample Testing with Analytic Representations of Probability Measures, in Advances in Neural Information Processing Systems (NIPS), vol. 28, 2015, 1981–1989.
  • D. Vukobratovic, D. Sejdinovic, A. Pizurica, Compressed Sensing Using Sparse Binary Measurements: A Rateless Coding Perspective, in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2015.
  • Z. Kurth-Nelson, G. Barnes, D. Sejdinovic, R. Dolan, P. Dayan, Temporal structure in associative retrieval, eLife, vol. 4, no. e04919, 2015.
  • W. Jitkrittum, A. Gretton, N. Heess, S. M. A. Eslami, B. Lakshminarayanan, D. Sejdinovic, Z. Szabó, Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages, in Uncertainty in Artificial Intelligence (UAI), 2015.

2014

  • K. Chwialkowski, D. Sejdinovic, A. Gretton, A Wild Bootstrap for Degenerate Kernel Tests, in Advances in Neural Information Processing Systems (NIPS), vol. 27, 2014, 3608–3616.
  • D. Sejdinovic, H. Strathmann, M. Lomeli, C. Andrieu, A. Gretton, Kernel Adaptive Metropolis-Hastings, in International Conference on Machine Learning (ICML), 2014, 1665–1673.
  • O. Johnson, D. Sejdinovic, J. Cruise, R. Piechocki, A. Ganesh, Non-Parametric Change-Point Estimation using String Matching Algorithms, Methodology and Computing in Applied Probability, vol. 16, no. 4, 987–1008, 2014.

2013

  • D. Sejdinovic, A. Gretton, W. Bergsma, A Kernel Test for Three-Variable Interactions, in Advances in Neural Information Processing Systems (NIPS), vol. 26, 2013, 1124–1132.
  • D. Sejdinovic, B. Sriperumbudur, A. Gretton, K. Fukumizu, Equivalence of distance-based and RKHS-based statistics in hypothesis testing, Annals of Statistics, vol. 41, no. 5, 2263–2291, Oct. 2013.

2012

  • A. Gretton, B. K. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil, K. Fukumizu, Optimal Kernel Choice for Large-Scale Two-Sample Tests, in Advances in Neural Information Processing Systems (NIPS), vol. 25, 2012, 1205–1213.
  • D. Sejdinovic, A. Gretton, B. K. Sriperumbudur, K. Fukumizu, Hypothesis testing using pairwise distances and associated kernels, in International Conference on Machine Learning (ICML), 2012, 1111–1118.
  • R. Piechocki, D. Sejdinovic, Combinatorial Channel Signature Modulation for Wireless ad-hoc Networks, in IEEE International Conference on Communications (ICC), 2012.
  • A. Muller, D. Sejdinovic, R. Piechocki, Approximate Message Passing under Finite Alphabet Constraints, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2012.

2011

  • W. Dai, D. Sejdinovic, O. Milenkovic, Gaussian Dynamic Compressive Sensing, in International Conference on Sampling Theory and Applications (SampTA), 2011.

2010

  • D. Sejdinovic, O. Johnson, Note on noisy group testing: asymptotic bounds and belief propagation reconstruction, in 48th Annual Allerton Conference on Communication, Control, and Computing, 2010, 998–1003.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, M. Ismail, Decentralised distributed fountain coding: asymptotic analysis and design, IEEE Communications Letters, vol. 14, no. 1, 42–44, 2010.
  • D. Sejdinovic, C. Andrieu, R. Piechocki, Bayesian sequential compressed sensing in sparse dynamical systems, in 48th Annual Allerton Conference on Communication, Control, and Computing, 2010, 1730–1736.

2009

  • D. Sejdinovic, D. Vukobratovic, A. Doufexi, V. Senk, R. Piechocki, Expanding window fountain codes for unequal error protection, IEEE Transactions on Communications, vol. 57, no. 9, 2510–2516, 2009.
  • D. Vukobratovic, V. Stankovic, D. Sejdinovic, L. Stankovic, Z. Xiong, Scalable video multicast using expanding window fountain codes, IEEE Transactions on Multimedia, vol. 11, no. 6, 1094–1104, 2009.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, M. Ismail, Fountain code design for data multicast with side information, IEEE Transactions on Wireless Communications, vol. 8, no. 10, 5155–5165, 2009.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, AND-OR tree analysis of distributed LT codes, in IEEE Information Theory Workshop (ITW), 2009, 261–265.
  • D. Vukobratovic, V. Stankovic, L. Stankovic, D. Sejdinovic, Precoded EWF codes for unequal error protection of scalable video, in International ICST Mobile Multimedia Communications Conference (MOBIMEDIA), 2009.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, Rateless distributed source code design, in International ICST Mobile Multimedia Communications Conference (MOBIMEDIA), 2009.
  • D. Sejdinovic, Topics in Fountain Coding, PhD thesis, University of Bristol, 2009.

2008

  • D. Vukobratovic, V. Stankovic, D. Sejdinovic, L. Stankovic, Z. Xiong, Expanding window fountain codes for scalable video multicast, in IEEE International Conference on Multimedia and Expo (ICME), 2008, 77–80.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, M. Ismail, Fountain coding with decoder side information, in IEEE International Conference on Communications (ICC), 2008, 4477–4482.
  • D. Sejdinovic, V. Ponnampalam, R. Piechocki, A. Doufexi, The throughput analysis of different IR-HARQ schemes based on fountain codes, in IEEE Wireless Communications and Networking Conference (WCNC), 2008, 267–272.
  • D. Sejdinovic, R. Piechocki, A. Doufexi, M. Ismail, Rate adaptive binary erasure quantization with dual fountain codes, in IEEE Global Telecommunications Conference (GLOBECOM), 2008.

2007

  • D. Vukobratovic, V. Stankovic, D. Sejdinovic, L. Stankovic, Z. Xiong, Scalable data multicast using expanding window fountain codes, in 45th Annual Allerton Conference on Communication, Control, and Computing, 2007.
  • D. Sejdinovic, D. Vukobratovic, A. Doufexi, V. Senk, R. Piechocki, Expanding window fountain codes for unequal error protection, in Asilomar Conference on Signals, Systems and Computers, 2007, 1020–1024.

Software

2017

  • S. Flaxman, Y. W. Teh, D. Sejdinovic, Kernel Poisson. 2017.
    Project: bigbayes