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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 Turing Fellow. I conduct research at the interface between machine learning and statistical methodology, with an emphasis on nonparametric and kernel methods.

Publications

2023

  • S. Bouabid , J. Fawkes , D. Sejdinovic , Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge, arXiv preprint arXiv:2301.11214, 2023.

2022

  • S. Bouabid , D. Watson-Parris , D. Sejdinovic , Bayesian inference for aerosol vertical profiles, in NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2022.
  • S. Bouabid , D. Watson-Parris , S. Stefanović , A. Nenes , D. Sejdinovic , AODisaggregation: toward global aerosol vertical profiles, arXiv preprint arXiv:2205.04296, 2022.
  • J. Fawkes , R. J. Evans , D. Sejdinovic , Selection, ignorability and challenges with causal fairness, in Conference on Causal Learning and Reasoning, 2022, 275–289.
  • J. Fawkes , R. Evans , D. Sejdinovic , Selection, Ignorability and Challenges With Causal Fairness, arXiv preprint arXiv:2202.13774, 2022.

2021

  • S. L. Chau , S. Bouabid , D. Sejdinovic , Deconditional Downscaling with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • S. L. Chau , J. Ton , J. Gonzalez , Y. W. Teh , D. Sejdinovic , BayesIMP: Uncertainty Quantification for Causal Data Fusion, in Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • R. Hu , G. K. Nicholls , D. Sejdinovic , Large Scale Tensor Regression using Kernels and Variational Inference, Machine Learning, 2021.
  • T. Fernandez , A. Gretton , D. Rindt , D. Sejdinovic , A Kernel Log-Rank Test of Independence for Right-Censored Data, Journal of the American Statistical Association, 2021.
  • V. Nguyen , S. B. Orbell , D. T. Lennon , H. Moon , F. Vigneau , L. C. Camenzind , L. Yu , D. M. Zumbühl , G. A. D. Briggs , M. A. Osborne , D. Sejdinovic , N. Ares , Deep Reinforcement Learning for Efficient Measurement of Quantum Devices, npj Quantum Information, vol. 7, no. 100, 2021.
  • A. Caterini , R. Cornish , D. Sejdinovic , A. Doucet , Variational Inference with Continuously-Indexed Normalizing Flows, in Uncertainty in Artificial Intelligence (UAI), 2021.
  • Z. Li , J. Ton , D. Oglic , D. Sejdinovic , Towards A Unified Analysis of Random Fourier Features, Journal of Machine Learning Research (JMLR), vol. 22, no. 108, 1–51, 2021.
  • X. Pu , S. L. Chau , X. Dong , D. Sejdinovic , Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint, IEEE Transactions on Signal and Information Processing over Networks, vol. 7, 192–207, 2021.
  • D. Rindt , D. Sejdinovic , D. Steinsaltz , Consistency of permutation tests of independence using distance covariance, HSIC and dHSIC, Stat, vol. 10, no. 1, e364, 2021.
  • J. Ton , L. Chan , Y. W. Teh , D. Sejdinovic , Noise Contrastive Meta Learning for Conditional Density Estimation using Kernel Mean Embeddings, in Artificial Intelligence and Statistics (AISTATS), 2021, PMLR 130:1099–1107.
  • J. Ton , D. Sejdinovic , K. Fukumizu , Meta Learning for Causal Direction, in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, no. 11, 9897–9905.
  • R. Hu , D. Sejdinovic , Robust Deep Interpretable Features for Binary Image Classification, in Proceedings of the Northern Lights Deep Learning Workshop, 2021, vol. 2.
  • G. S. Blair , R. Bassett , L. Bastin , L. Beevers , M. I. Borrajo , M. Brown , S. L. Dance , A. Dionescu , L. Edwards , M. A. Ferrario , R. Fraser , H. Fraser , S. Gardner , P. Henrys , T. Hey , S. Homann , C. Huijbers , J. Hutchison , P. Jonathan , R. Lamb , S. Laurie , A. Leeson , D. Leslie , M. McMillan , V. Nundloll , O. Oyebamiji , J. Phillipson , V. Pope , R. Prudden , S. Reis , M. Salama , F. Samreen , D. Sejdinovic , W. Simm , R. Street , L. Thornton , R. Towe , J. V. Hey , M. Vieno , J. Waller , J. Watkins , The Role of Digital Technologies in Responding to the Grand Challenges of the Natural Environment: The Windermere Accord, Patterns, vol. 2, no. 1, 100156, 2021.

2020

  • D. Rindt , D. Sejdinovic , D. Steinsaltz , A kernel and optimal transport based test of independence between covariates and right-censored lifetimes, International Journal of Biostatistics, 2020.
  • N. M. Esbroeck , D. T. Lennon , H. Moon , V. Nguyen , F. Vigneau , L. C. Camenzind , L. Yu , D. Zumbuehl , G. A. D. Briggs , D. Sejdinovic , N. Ares , Quantum device fine-tuning using unsupervised embedding learning, New Journal of Physics, vol. 22, no. 9, 095003, 2020.
  • H. Moon , D. T. Lennon , J. Kirkpatrick , N. M. Esbroeck , L. C. Camenzind , L. Yu , F. Vigneau , D. M. Zumbühl , G. A. D. Briggs , M. A. Osborne , D. Sejdinovic , E. A. Laird , N. Ares , Machine learning enables completely automatic tuning of a quantum device faster than human experts, Nature Communications, vol. 11, no. 4161, 2020.
  • T. Rudner , D. Sejdinovic , Y. Gal , Inter-domain Deep Gaussian Processes, in International Conference on Machine Learning (ICML), 2020, PMLR 119:8286–8294.
  • D. Sejdinovic , Discussion of ‘Functional models for time-varying random objects’ by Dubey and Müller, Journal of the Royal Statistical Society: Series B, vol. 82, no. 2, 312–313, 2020.

2019

  • H. Law , P. Zhao , L. Chan , J. Huang , D. Sejdinovic , Hyperparameter Learning via Distributional Transfer, Advances in Neural Information Processing Systems (NeurIPS), to appear, 2019.
    Project: tencent-lsml
  • A. Raj , H. Law , D. Sejdinovic , M. Park , A Differentially Private Kernel Two-Sample Test, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2019, to appear.
    Project: bigbayes
  • D. Watson-Parris , S. Sutherland , M. Christensen , A. Caterini , D. Sejdinovic , P. Stier , Detecting Anthropogenic Cloud Perturbations with Deep Learning, in ICML 2019 Workshop on Climate Change: How Can AI Help?, 2019.
  • J. Runge , P. Nowack , M. Kretschmer , S. Flaxman , D. Sejdinovic , Detecting and Quantifying Causal Associations in Large Nonlinear Time Series Datasets, Science Advances, vol. 5, no. 11, 2019.
  • Z. Li , A. Perez-Suay , G. Camps-Valls , D. Sejdinovic , Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness, ArXiv e-prints:1911.04322, 2019.
  • D. Rindt , D. Sejdinovic , D. Steinsaltz , Nonparametric Independence Testing for Right-Censored Data using Optimal Transport, ArXiv e-prints:1906.03866, 2019.
  • J. Ton , L. Chan , Y. W. Teh , D. Sejdinovic , Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings, ArXiv e-prints:1906.02236, 2019.
    Project: bigbayes tencent-lsml
  • G. Camps-Valls , D. Sejdinovic , J. Runge , M. Reichstein , A Perspective on Gaussian Processes for Earth Observation, National Science Review, 2019.
  • Z. Li , J. Ton , D. Oglic , D. Sejdinovic , Towards A Unified Analysis of Random Fourier Features, in International Conference on Machine Learning (ICML), 2019, PMLR 97:3905–3914.
  • F. Briol , C. Oates , M. Girolami , M. Osborne , D. Sejdinovic , Probabilistic Integration: A Role in Statistical Computation? (with Discussion and Rejoinder), Statistical Science, vol. 34, no. 1, 1–22; rejoinder: 38–42, 2019.

2018

  • J. Mitrovic , D. Sejdinovic , Y. Teh , Causal Inference via Kernel Deviance Measures, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • Q. Zhang , S. Filippi , A. Gretton , D. Sejdinovic , Large-Scale Kernel Methods for Independence Testing, Statistics and Computing, vol. 28, no. 1, 113–130, Jan. 2018.
    Project: bigbayes
  • A. Caterini , A. Doucet , D. Sejdinovic , Hamiltonian Variational Auto-Encoder, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
  • H. Law , D. Sejdinovic , E. Cameron , T. Lucas , S. Flaxman , K. Battle , K. Fukumizu , Variational Learning on Aggregate Outputs with Gaussian Processes, in Advances in Neural Information Processing Systems (NeurIPS), 2018, to appear.
    Project: bigbayes
  • H. C. L. Law , P. Zhao , J. Huang , D. Sejdinovic , Hyperparameter Learning via Distributional Transfer, ArXiv e-prints:1810.06305, 2018.
    Project: tencent-lsml
  • M. Kanagawa , P. Hennig , D. Sejdinovic , B. Sriperumbudur , Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences, ArXiv e-prints:1807.02582, 2018.
  • J. Ton , S. Flaxman , D. Sejdinovic , S. Bhatt , Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features, Spatial Statistics, vol. 28, 59–78, 2018.
    Project: bigbayes
  • H. Law , D. Sutherland , D. Sejdinovic , S. Flaxman , Bayesian Approaches to Distribution Regression, in Artificial Intelligence and Statistics (AISTATS), 2018.
    Project: bigbayes

2017

  • T. G. J. Rudner , D. Sejdinovic , Inter-domain Deep Gaussian Processes, NeurIPS 2017 Workshop on Bayesian Deep Learning, 2017.
  • S. Flaxman , Y. Teh , D. Sejdinovic , Poisson Intensity Estimation with Reproducing Kernels, Electronic Journal of Statistics, vol. 11, no. 2, 5081–5104, 2017.
    Project: bigbayes
  • 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.
    Project: bigbayes
  • H. Law , C. Yau , D. Sejdinovic , Testing and Learning on Distributions with Symmetric Noise Invariance, in Advances in Neural Information Processing Systems (NeurIPS), 2017, 1343–1353.
  • J. Runge , P. Nowack , M. Kretschmer , S. Flaxman , D. Sejdinovic , 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, in 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

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

  • 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 (NeurIPS), 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 (NeurIPS), 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 (NeurIPS), 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 , 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.
  • D. Sejdinovic , A. Gretton , W. Bergsma , A Kernel Test for Three-Variable Interactions, in Advances in Neural Information Processing Systems (NeurIPS), vol. 26, 2013, 1124–1132.

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 (NeurIPS), 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