Dino Sejdinovic

Instrumental and Proximal Causal Inference with Gaussian Processes UAI 2026 The Generalised Kernel Covariance Measure CLeaR 2026 Bayesian Low-Rank Learning (Bella): A Practical Approach to Bayesian Neural Networks AAAI 2025 Credal Two-Sample Tests of Epistemic Uncertainty AISTATS 2025 Label Distribution Learning using the Squared Neural Family on the Probability Simplex UAI 2025 A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment JMLR 2024 Bayesian Adaptive Calibration and Optimal Design NeurIPS 2024 Exact, Fast and Expressive Poisson Point Processes via Squared Neural Families AAAI 2024 Neural-Kernel Conditional Mean Embeddings ICML 2024 A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods NeurIPS 2023 Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models NeurIPS 2023 Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge ICML 2023 Squared Neural Families: A New Class of Tractable Density Models NeurIPS 2023 Explaining Preferences with Shapley Values NeurIPS 2022 Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning NeurIPS 2022 Giga-scale Kernel Matrix-Vector Multiplication on GPU NeurIPS 2022 Learning Inconsistent Preferences with Gaussian Processes AISTATS 2022 RKHS-SHAP: Shapley Values for Kernel Methods NeurIPS 2022 Selection, Ignorability and Challenges With Causal Fairness CLeaR 2022 Survival regression with proper scoring rules and monotonic neural networks AISTATS 2022 BayesIMP: Uncertainty Quantification for Causal Data Fusion NeurIPS 2021 Deconditional Downscaling with Gaussian Processes NeurIPS 2021 Meta Learning for Causal Direction AAAI 2021 Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings AISTATS 2021 Towards a Unified Analysis of Random Fourier Features JMLR 2021 Variational inference with continuously-indexed normalizing flows UAI 2021 Inter-domain Deep Gaussian Processes ICML 2020 Hyperparameter Learning via Distributional Transfer NeurIPS 2019 Towards a Unified Analysis of Random Fourier Features ICML 2019 Bayesian Approaches to Distribution Regression AISTATS 2018 Causal Inference via Kernel Deviance Measures NeurIPS 2018 Hamiltonian Variational Auto-Encoder NeurIPS 2018 Variational Learning on Aggregate Outputs with Gaussian Processes NeurIPS 2018 Poisson intensity estimation with reproducing kernels AISTATS 2017 Testing and Learning on Distributions with Symmetric Noise Invariance NeurIPS 2017 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression ICML 2016 K2-ABC: Approximate Bayesian Computation with Kernel Embeddings AISTATS 2016 Fast Two-Sample Testing with Analytic Representations of Probability Measures NeurIPS 2015 Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families NeurIPS 2015 A Wild Bootstrap for Degenerate Kernel Tests NeurIPS 2014 Kernel Adaptive Metropolis-Hastings ICML 2014 A Kernel Test for Three-Variable Interactions NeurIPS 2013 Optimal kernel choice for large-scale two-sample tests NeurIPS 2012