← Authors

Samuel Kaski

71 papers · 2010–2026 · 12 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (21) 🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer πŸƒ Academic Marathon (15) 🌍 Conference Polyglot (12) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ”¬ Deep Specialist (26) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ† Grand Slam 🀝 Dynamic Duo (15) πŸ’Ž Century Club (70) ❓ The Questioner (2) πŸš€ Conference Pioneer ⚑ Prolific Year (5) πŸ”₯ Unstoppable (16) πŸ—ƒοΈ Keyword Collector (61) πŸ“ˆ Trend Setter

Conferences

AISTATS (16) NIPS (15) ICML (11) UAI (8) JMLR (5) AAAI (4) ICLR (4) IJCAI (3) ACML (2) CVPR (1) MLHC (1) PGM (1)

Top co-authors

Markus Heinonen (15) Diego Mesquita (8) Luigi Acerbi (8) Jaakko Peltonen (6) Daolang Huang (6) Ayush Bharti (6) Julien Martinelli (5) Vikas Garg (5) Harri Lahdesmaki (4) Sabina J. Sloman (3)

Research topics

Differential Privacy (1)

Keywords

bayesian inference (17) gaussian process (10) variational inference (8) active learning (5) kernel methods (5) latent variable model (5) dimensionality reduction (4) graph neural network (4) likelihood-free inference (4) markov chain monte carlo (4) differential privacy (3) neural network (3) approximate bayesian computation (3) probabilistic modeling (3) bayesian optimization (3) non-stationary kernel (3) data visualization (2) synthetic data generation (2) parallel computing (2) optimal transport (2)

Papers

More than Irrational: Modeling Belief-Biased Agents AAAI 2026
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport CVPR 2025
Cost-aware simulation-based inference AISTATS 2025
Amortized Probabilistic Conditioning for Optimization, Simulation and Inference AISTATS 2025
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? AISTATS 2025
PABBO: Preferential Amortized Black-Box Optimization ICLR 2025
When do GFlowNets learn the right distribution? ICLR 2025
Privacy-Preserving Neural Processes for Probabilistic User Modeling UAI 2025
Generalization and Distributed Learning of GFlowNets ICLR 2025
Proxy-informed Bayesian transfer learning with unknown sources UAI 2025
Learning relevant contextual variables within Bayesian optimization UAI 2024
Improving robustness to corruptions with multiplicative weight perturbations NIPS 2024
Amortized Bayesian Experimental Design for Decision-Making NIPS 2024
Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice NIPS 2024
Estimating treatment effects from single-arm trials via latent-variable modeling AISTATS 2024
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series NIPS 2024
Open Ad Hoc Teamwork with Cooperative Game Theory ICML 2024
Embarrassingly Parallel GFlowNets ICML 2024
Input-gradient space particle inference for neural network ensembles ICLR 2024
Bayesian Active Learning in the Presence of Nuisance Parameters UAI 2024
Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference ICML 2023
Learning Robust Statistics for Simulation-based Inference under Model Misspecification NIPS 2023
Compositional Sculpting of Iterative Generative Processes NIPS 2023
Practical Equivariances via Relational Conditional Neural Processes NIPS 2023
Teaching to Learn: Sequential Teaching of Learners with Internal States AAAI 2023
Zero-Shot Assistance in Sequential Decision Problems AAAI 2023
Differentiable user models UAI 2023
Characterizing personalized effects of family information on disease risk using graph representation learning MLHC 2023
Noise-Aware Statistical Inference with Differentially Private Synthetic Data AISTATS 2023
Multi-Fidelity Bayesian Optimization with Unreliable Information Sources AISTATS 2023
Non-separable Spatio-temporal Graph Kernels via SPDEs AISTATS 2022
Tackling covariate shift with node-based Bayesian neural networks ICML 2022
Deconfounded Representation Similarity for Comparison of Neural Networks NIPS 2022
Modular Flows: Differential Molecular Generation NIPS 2022
Parallel MCMC Without Embarrassing Failures AISTATS 2022
Approximate Bayesian Computation with Domain Expert in the Loop ICML 2022
Variational multiple shooting for Bayesian ODEs with Gaussian processes UAI 2022
Provably expressive temporal graph networks NIPS 2022
Federated stochastic gradient Langevin dynamics UAI 2021
De-randomizing MCMC dynamics with the diffusion Stein operator NIPS 2021
Bayesian Inference for Optimal Transport with Stochastic Cost ACML 2021
Differentially Private Bayesian Inference for Generalized Linear Models ICML 2021
Learning spectrograms with convolutional spectral kernels AISTATS 2020
Rethinking pooling in graph neural networks NIPS 2020
Scalable Probabilistic Matrix Factorization with Graph-Based Priors AAAI 2020
Projective Preferential Bayesian Optimization ICML 2020
Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets IJCAI 2019
Scalable Bayesian Non-linear Matrix Completion IJCAI 2019
Machine Teaching of Active Sequential Learners NIPS 2019
Embarrassingly Parallel MCMC using Deep Invertible Transformations UAI 2019
Active Learning for Decision-Making from Imbalanced Observational Data ICML 2019
Harmonizable mixture kernels with variational Fourier features AISTATS 2019
Deep learning with differential Gaussian process flows AISTATS 2019
ELFI: Engine for Likelihood-Free Inference JMLR 2018
Differentially private Bayesian learning on distributed data NIPS 2017
Non-Stationary Spectral Kernels NIPS 2017
GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis JMLR 2017
Localized Lasso for High-Dimensional Regression AISTATS 2017
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings ACML 2017
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo AISTATS 2016
A Robust Convex Formulation for Ensemble Clustering IJCAI 2016
Bayesian Networks for Variable Groups PGM 2016
Multiple Output Regression with Latent Noise JMLR 2016
Majorization-Minimization for Manifold Embedding AISTATS 2015
Optimization Equivalence of Divergences Improves Neighbor Embedding ICML 2014
Kernelized Bayesian Matrix Factorization ICML 2013
Bayesian Canonical Correlation Analysis JMLR 2013
Scalable Optimization of Neighbor Embedding for Visualization ICML 2013
Bayesian Group Factor Analysis AISTATS 2012
Generative Modeling for Maximizing Precision and Recall in Information Visualization AISTATS 2011
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization JMLR 2010