Samuel Kaski

More than Irrational: Modeling Belief-Biased Agents AAAI 2026 Robust Bayesian Decision Making under Adversarial Uncertainty UAI 2026 Amortized Probabilistic Conditioning for Optimization, Simulation and Inference AISTATS 2025 Cost-aware simulation-based inference AISTATS 2025 From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport CVPR 2025 Generalization and Distributed Learning of GFlowNets ICLR 2025 PABBO: Preferential Amortized Black-Box Optimization ICLR 2025 Privacy-Preserving Neural Processes for Probabilistic User Modeling UAI 2025 Proxy-informed Bayesian transfer learning with unknown sources UAI 2025 What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much? AISTATS 2025 When do GFlowNets learn the right distribution? ICLR 2025 Amortized Bayesian Experimental Design for Decision-Making NeurIPS 2024 Bayesian Active Learning in the Presence of Nuisance Parameters UAI 2024 Embarrassingly Parallel GFlowNets ICML 2024 Estimating treatment effects from single-arm trials via latent-variable modeling AISTATS 2024 Improving robustness to corruptions with multiplicative weight perturbations NeurIPS 2024 Input-gradient space particle inference for neural network ensembles ICLR 2024 Learning relevant contextual variables within Bayesian optimization UAI 2024 Open Ad Hoc Teamwork with Cooperative Game Theory ICML 2024 Preference Learning of Latent Decision Utilities with a Human-like Model of Preferential Choice NeurIPS 2024 TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series NeurIPS 2024 Characterizing personalized effects of family information on disease risk using graph representation learning MLHC 2023 Compositional Sculpting of Iterative Generative Processes NeurIPS 2023 Differentiable user models UAI 2023 Learning Robust Statistics for Simulation-based Inference under Model Misspecification NeurIPS 2023 Multi-Fidelity Bayesian Optimization with Unreliable Information Sources AISTATS 2023 Noise-Aware Statistical Inference with Differentially Private Synthetic Data AISTATS 2023 Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference ICML 2023 Practical Equivariances via Relational Conditional Neural Processes NeurIPS 2023 Teaching to Learn: Sequential Teaching of Learners with Internal States AAAI 2023 Zero-Shot Assistance in Sequential Decision Problems AAAI 2023 Approximate Bayesian Computation with Domain Expert in the Loop ICML 2022 Deconfounded Representation Similarity for Comparison of Neural Networks NeurIPS 2022 Modular Flows: Differential Molecular Generation NeurIPS 2022 Non-separable Spatio-temporal Graph Kernels via SPDEs AISTATS 2022 Parallel MCMC Without Embarrassing Failures AISTATS 2022 Provably expressive temporal graph networks NeurIPS 2022 Tackling covariate shift with node-based Bayesian neural networks ICML 2022 Variational multiple shooting for Bayesian ODEs with Gaussian processes UAI 2022 Bayesian Inference for Optimal Transport with Stochastic Cost ACML 2021 De-randomizing MCMC dynamics with the diffusion Stein operator NeurIPS 2021 Differentially Private Bayesian Inference for Generalized Linear Models ICML 2021 Federated stochastic gradient Langevin dynamics UAI 2021 Learning spectrograms with convolutional spectral kernels AISTATS 2020 Projective Preferential Bayesian Optimization ICML 2020 Rethinking pooling in graph neural networks NeurIPS 2020 Scalable Probabilistic Matrix Factorization with Graph-Based Priors AAAI 2020 Active Learning for Decision-Making from Imbalanced Observational Data ICML 2019 Deep learning with differential Gaussian process flows AISTATS 2019 Embarrassingly Parallel MCMC using Deep Invertible Transformations UAI 2019 Harmonizable mixture kernels with variational Fourier features AISTATS 2019 Human-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets IJCAI 2019 Machine Teaching of Active Sequential Learners NeurIPS 2019 Scalable Bayesian Non-linear Matrix Completion IJCAI 2019 ELFI: Engine for Likelihood-Free Inference JMLR 2018 A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings ACML 2017 Differentially private Bayesian learning on distributed data NeurIPS 2017 GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis JMLR 2017 Localized Lasso for High-Dimensional Regression AISTATS 2017 Non-Stationary Spectral Kernels NeurIPS 2017 A Robust Convex Formulation for Ensemble Clustering IJCAI 2016 Bayesian Networks for Variable Groups PGM 2016 Multiple Output Regression with Latent Noise JMLR 2016 Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo AISTATS 2016 Majorization-Minimization for Manifold Embedding AISTATS 2015 Optimization Equivalence of Divergences Improves Neighbor Embedding ICML 2014 Bayesian Canonical Correlation Analysis JMLR 2013 Kernelized Bayesian Matrix Factorization ICML 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 Information Retrieval by Inferring Implicit Queries from Eye Movements AISTATS 1999 Nonlinear Dimensionality Reduction as Information Retrieval AISTATS 1999