Sejdinovic, Dino

54 publications

AAAI 2025 Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks Bao Gia Doan, Afshar Shamsi, Xiao-Yu Guo, Arash Mohammadi, Hamid Alinejad-Rokny, Dino Sejdinovic, Damien Teney, Damith C. Ranasinghe, Ehsan Abbasnejad

AISTATS 2025 Credal Two-Sample Tests of Epistemic Uncertainty Siu Lun Chau, Antonin Schrab, Arthur Gretton, Dino Sejdinovic, Krikamol Muandet

UAI 2025 Label Distribution Learning Using the Squared Neural Family on the Probability Simplex Daokun Zhang, Russell Tsuchida, Dino Sejdinovic

NeurIPS 2025 Squared Families Are Useful Conjugate Priors Russell Tsuchida, Jiawei Liu, Cheng Soon Ong, Dino Sejdinovic

JMLR 2024 A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment Robert Hu, Dino Sejdinovic, Robin J. Evans

NeurIPS 2024 Bayesian Adaptive Calibration and Optimal Design Rafael Oliveira, Dino Sejdinovic, David Howard, Edwin V. Bonilla

TMLR 2024 Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Jake Fawkes, Robert Hu, Robin J. Evans, Dino Sejdinovic

AAAI 2024 Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic

ICML 2024 Neural-Kernel Conditional Mean Embeddings Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic

NeurIPS 2023 A Rigorous Link Between Deep Ensembles and (Variational) Bayesian Methods Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic, Jeremias Knoblauch

NeurIPS 2023 Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic

TMLR 2023 Fair Kernel Regression Through Cross-Covariance Operators Adrian Perez-Suay, Paula Gordaliza, Jean-Michel Loubes, Dino Sejdinovic, Gustau Camps-Valls

ICML 2023 Returning the Favour: When Regression Benefits from Probabilistic Causal Knowledge Shahine Bouabid, Jake Fawkes, Dino Sejdinovic

NeurIPS 2023 Squared Neural Families: A New Class of Tractable Density Models Russell Tsuchida, Cheng Soon Ong, Dino Sejdinovic

AISTATS 2022 Learning Inconsistent Preferences with Gaussian Processes Siu Lun Chau, Javier Gonzalez, Dino Sejdinovic

AISTATS 2022 Survival Regression with Proper Scoring Rules and Monotonic Neural Networks David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic

NeurIPS 2022 Explaining Preferences with Shapley Values Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas, Dino Sejdinovic

NeurIPS 2022 Generalized Variational Inference in Function Spaces: Gaussian Measures Meet Bayesian Deep Learning Veit David Wild, Robert Hu, Dino Sejdinovic

NeurIPS 2022 Giga-Scale Kernel Matrix-Vector Multiplication on GPU Robert Hu, Siu Lun Chau, Dino Sejdinovic, Joan Glaunès

MLJ 2022 Large Scale Tensor Regression Using Kernels and Variational Inference Robert Hu, Geoff K. Nicholls, Dino Sejdinovic

NeurIPS 2022 RKHS-SHAP: Shapley Values for Kernel Methods Siu Lun Chau, Robert Hu, Javier González, Dino Sejdinovic

CLeaR 2022 Selection, Ignorability and Challenges with Causal Fairness Jake Fawkes, Robin Evans, Dino Sejdinovic

ECML-PKDD 2022 Spectral Ranking with Covariates Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic

AISTATS 2021 Noise Contrastive Meta-Learning for Conditional Density Estimation Using Kernel Mean Embeddings Jean-Francois Ton, Lucian Chan, Yee Whye Teh, Dino Sejdinovic

NeurIPS 2021 BayesIMP: Uncertainty Quantification for Causal Data Fusion Siu Lun Chau, Jean-Francois Ton, Javier González, Yee W. Teh, Dino Sejdinovic

NeurIPS 2021 Deconditional Downscaling with Gaussian Processes Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic

AAAI 2021 Meta Learning for Causal Direction Jean-François Ton, Dino Sejdinovic, Kenji Fukumizu

JMLR 2021 Towards a Unified Analysis of Random Fourier Features Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

UAI 2021 Variational Inference with Continuously-Indexed Normalizing Flows Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet

ICML 2020 Inter-Domain Deep Gaussian Processes Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

ECML-PKDD 2019 A Differentially Private Kernel Two-Sample Test Anant Raj, Ho Chung Leon Law, Dino Sejdinovic, Mijung Park

NeurIPS 2019 Hyperparameter Learning via Distributional Transfer Ho Chung Law, Peilin Zhao, Leung Sing Chan, Junzhou Huang, Dino Sejdinovic

ICML 2019 Towards a Unified Analysis of Random Fourier Features Zhu Li, Jean-Francois Ton, Dino Oglic, Dino Sejdinovic

AISTATS 2018 Bayesian Approaches to Distribution Regression Ho Chung Leon Law, Danica J. Sutherland, Dino Sejdinovic, Seth R. Flaxman

NeurIPS 2018 Causal Inference via Kernel Deviance Measures Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

NeurIPS 2018 Hamiltonian Variational Auto-Encoder Anthony L Caterini, Arnaud Doucet, Dino Sejdinovic

NeurIPS 2018 Variational Learning on Aggregate Outputs with Gaussian Processes Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu

ICLR 2017 Deep Kernel Machines via the Kernel Reparametrization Trick Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh

UAI 2017 Feature-to-Feature Regression for a Two-Step Conditional Independence Test Qinyi Zhang, Sarah Filippi, Seth R. Flaxman, Dino Sejdinovic

ECML-PKDD 2017 Kernel Sequential Monte Carlo Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

AISTATS 2017 Poisson Intensity Estimation with Reproducing Kernels Seth R. Flaxman, Yee Whye Teh, Dino Sejdinovic

NeurIPS 2017 Testing and Learning on Distributions with Symmetric Noise Invariance Ho Chung Law, Christopher Yau, Dino Sejdinovic

UAI 2016 Bayesian Learning of Kernel Embeddings Seth R. Flaxman, Dino Sejdinovic, John P. Cunningham, Sarah Filippi

ICML 2016 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression Jovana Mitrovic, Dino Sejdinovic, Yee-Whye Teh

AISTATS 2016 K2-ABC: Approximate Bayesian Computation with Kernel Embeddings Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic

UAI 2016 Super-Sampling with a Reservoir Brooks Paige, Dino Sejdinovic, Frank D. Wood

NeurIPS 2015 Fast Two-Sample Testing with Analytic Representations of Probability Measures Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic, Arthur Gretton

NeurIPS 2015 Gradient-Free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltan Szabo, Arthur Gretton

UAI 2015 Kernel-Based Just-in-Time Learning for Passing Expectation Propagation Messages Wittawat Jitkrittum, Arthur Gretton, Nicolas Heess, S. M. Ali Eslami, Balaji Lakshminarayanan, Dino Sejdinovic, Zoltán Szabó

NeurIPS 2014 A Wild Bootstrap for Degenerate Kernel Tests Kacper P Chwialkowski, Dino Sejdinovic, Arthur Gretton

ICML 2014 Kernel Adaptive Metropolis-Hastings Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

NeurIPS 2013 A Kernel Test for Three-Variable Interactions Dino Sejdinovic, Arthur Gretton, Wicher Bergsma

ICML 2012 Hypothesis Testing Using Pairwise Distances and Associated Kernels Dino Sejdinovic, Arthur Gretton, Bharath K. Sriperumbudur, Kenji Fukumizu

NeurIPS 2012 Optimal Kernel Choice for Large-Scale Two-Sample Tests Arthur Gretton, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu, Bharath K. Sriperumbudur