← Authors

Dino Sejdinovic

41 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (13) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (7) 🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (13) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (20) πŸ”¬ Deep Specialist (13) πŸ—ƒοΈ Keyword Collector (169) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (41) πŸ”₯ Unstoppable (14) ⚑ Prolific Year (7)

Conferences

NIPS (20) AISTATS (7) ICML (6) AAAI (3) JMLR (2) UAI (2) CLEAR (1)

Top co-authors

Siu Lun Chau (8) Arthur Gretton (7) Robert Hu (6) Jean-Francois Ton (5) Kenji Fukumizu (4) Russell Tsuchida (3) Seth Flaxman (3) Javier Gonzalez (3) Yee Whye Teh (3) Heiko Strathmann (3)

Research topics

Statistics (2)

Keywords

kernel methods (15) reproducing kernel hilbert space (10) gaussian process (9) bayesian inference (7) variational inference (7) uncertainty quantification (6) two-sample test (4) causal inference (4) markov chain monte carlo (3) kernel mean embedding (3) random fourier feature (3) kernel embedding (3) maximum mean discrepancy (3) approximate bayesian computation (3) shapley value (3) posterior approximation (2) bayesian optimization (2) density estimation (2) hamiltonian monte carlo (2) posterior inference (2)

Papers

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