Dino Sejdinovic
41 papers · 2012–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (7) π Interdisciplinary Bridge π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (13) π Academic Marathon (13)
πΊοΈ
Taxonomy Completionist
(13)
π§
Keyword Pioneer
π
Interdisciplinary Bridge
π
Conference Loyalist
(20)
π¬
Deep Specialist
(13)
ποΈ
Keyword Collector
(169)
π
Conference Pioneer
π
Trend Setter
π
Century Club
(41)
π₯
Unstoppable
(14)
β‘
Prolific Year
(7)
Conferences
NIPS (20)
AISTATS (7)
ICML (6)
AAAI (3)
JMLR (2)
UAI (2)
CLEAR (1)
Top co-authors
Research topics
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