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Dino Sejdinovic

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

Achievements

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+11 more ↓ 🌍 Conference Polyglot (7) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer πŸ—ΊοΈ Taxonomy Completionist (13) πŸƒ Academic Marathon (13)
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)

Research topics

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