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Dino Sejdinovic

Time-to-event regression using partially monotonic neural networks


Mar 26, 2021
David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic


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Inter-domain Deep Gaussian Processes


Nov 01, 2020
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

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* Published in Proceedings of the 37th International Conference on Machine Learning (ICML 2020) 
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Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint


Aug 23, 2020
Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic

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* 13 pages, with extra 3-page appendices 
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Benign Overfitting and Noisy Features


Aug 06, 2020
Zhu Li, Weijie Su, Dino Sejdinovic

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Variational Inference with Continuously-Indexed Normalizing Flows


Jul 10, 2020
Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet

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* To appear in the proceedings of the second workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models at ICML 2020 
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Meta Learning for Causal Direction


Jul 06, 2020
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu

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A Perspective on Gaussian Processes for Earth Observation


Jul 02, 2020
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein

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* National Science Review, Volume 6, Issue 4, July 2019, Pages 616-618 
 * 1 figure 
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Learning Inconsistent Preferences with Kernel Methods


Jun 06, 2020
Siu Lun Chau, Javier González, Dino Sejdinovic

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Spectral Ranking with Covariates


May 13, 2020
Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic

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Large Scale Tensor Regression using Kernels and Variational Inference


Feb 11, 2020
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic

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A kernel log-rank test of independence for right-censored data


Dec 08, 2019
Tamara Fernandez, Arthur Gretton, David Rindt, Dino Sejdinovic

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Detecting anthropogenic cloud perturbations with deep learning


Nov 29, 2019
Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony Caterini, Dino Sejdinovic, Philip Stier

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* Awarded Best Paper and Spotlight Oral at Climate Change: How Can AI Help? (Workshop) at International Conference on Machine Learning (ICML), Long Beach, California, 2019 
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Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness


Nov 11, 2019
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls, Dino Sejdinovic

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Nonparametric Independence Testing for Right-Censored Data using Optimal Transport


Jun 10, 2019
David Rindt, Dino Sejdinovic, David Steinsaltz

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Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings


Jun 05, 2019
Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic

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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"


Nov 26, 2018
Francois-Xavier Briol, Chris J. Oates, Mark Girolami, Michael A. Osborne, Dino Sejdinovic

* Accepted to Statistical Science 
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Hyperparameter Learning via Distributional Transfer


Oct 15, 2018
Ho Chung Leon Law, Peilin Zhao, Junzhou Huang, Dino Sejdinovic

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A Differentially Private Kernel Two-Sample Test


Aug 01, 2018
Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

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Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences


Jul 06, 2018
Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K Sriperumbudur

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* 64 pages 
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A Unified Analysis of Random Fourier Features


Jun 24, 2018
Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

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Hamiltonian Variational Auto-Encoder


May 29, 2018
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic

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* Submitted to NIPS 2018 
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Variational Learning on Aggregate Outputs with Gaussian Processes


May 22, 2018
Ho Chung Leon Law, Dino Sejdinovic, Ewan Cameron, Tim CD Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu

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Causal Inference via Kernel Deviance Measures


Apr 12, 2018
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

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Bayesian Approaches to Distribution Regression


Feb 22, 2018
Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic, Seth Flaxman

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* Final version to be published at AISTATS 2018 
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Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features


Nov 15, 2017
Jean-Francois Ton, Seth Flaxman, Dino Sejdinovic, Samir Bhatt

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* under submission to Spatial Statistics Journal 
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Testing and Learning on Distributions with Symmetric Noise Invariance


Nov 05, 2017
Ho Chung Leon Law, Christopher Yau, Dino Sejdinovic

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* 22 pages 
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Probabilistic Integration: A Role in Statistical Computation?


Oct 18, 2017
François-Xavier Briol, Chris. J. Oates, Mark Girolami, Michael A. Osborne, Dino Sejdinovic

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* Several improvements suggested by reviewers, including additional experiments on uncertainty quantification properties. Change of title: previously "Probabilistic Integration: A Role for Statisticians in Numerical Analysis?" 
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Kernel Sequential Monte Carlo


Jul 25, 2017
Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

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* ECML-PKDD 2017 
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Poisson intensity estimation with reproducing kernels


Jun 26, 2017
Seth Flaxman, Yee Whye Teh, Dino Sejdinovic

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* AISTATS 2017 
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