Markus Heinonen
35 papers · 2016–2025 · 7 conferences · across top CS/AI conferences
Achievements
Jump to papers ↓+13 more ↓ Show less ↑
π§ Keyword Pioneer π Conference Polyglot (7) πΊοΈ Taxonomy Completionist (12) π Interdisciplinary Bridge π Academic Marathon (9)
π£
Hot Topic Early Bird
πΊοΈ
Taxonomy Completionist
(12)
π§
Keyword Pioneer
π€
Dynamic Duo
(15)
π
Triple Crown
π¬
Deep Specialist
(11)
π§¬
Topic Evolution
π
Trend Setter
β‘
Prolific Year
(5)
β
The Questioner
ποΈ
Keyword Collector
(107)
π
Century Club
(35)
π₯
Unstoppable
(10)
Conferences
ICLR (9)
AISTATS (8)
NIPS (7)
ICML (6)
ACML (3)
CVPR (1)
UAI (1)
Top co-authors
Keywords
variational inference
(7)
gaussian process
(6)
bayesian inference
(5)
bayesian neural network
(5)
ordinary differential equation
(3)
markov chain monte carlo
(3)
neural network
(3)
non-stationary kernel
(3)
kernel methods
(3)
molecular generation
(2)
graph neural network
(2)
optimal transport
(2)
generative model
(2)
epistemic uncertainty
(2)
uncertainty quantification
(1)
corruption robustness
(1)
deep learning
(1)
dirichlet process
(1)
graph generation
(1)
prior knowledge
(1)
Papers
Robust Classification by Coupling Data Mollification with Label Smoothing
AISTATS 2025
Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models
ICML 2025
Progressive Tempering Sampler with Diffusion
ICML 2025
Diffusion Models as Cartoonists: The Curious Case of High Density Regions
ICLR 2025
Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
ICLR 2025
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
ICLR 2025
Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs
ICLR 2025
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?
AISTATS 2025
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
CVPR 2025
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
ICLR 2024
Improving robustness to corruptions with multiplicative weight perturbations
NIPS 2024
Input-gradient space particle inference for neural network ensembles
ICLR 2024
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
AISTATS 2023
Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States
NIPS 2023
Continuous-Time Functional Diffusion Processes
NIPS 2023
AbODE: Ab initio antibody design using conjoined ODEs
ICML 2023
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
ICLR 2023
Generative Modelling with Inverse Heat Dissipation
ICLR 2023
Variational multiple shooting for Bayesian ODEs with Gaussian processes
UAI 2022
Modular Flows: Differential Molecular Generation
NIPS 2022
Tackling covariate shift with node-based Bayesian neural networks
ICML 2022
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
AISTATS 2021
Bayesian Inference for Optimal Transport with Stochastic Cost
ACML 2021
Continuous-time Model-based Reinforcement Learning
ICML 2021
De-randomizing MCMC dynamics with the diffusion Stein operator
NIPS 2021
Learning continuous-time PDEs from sparse data with graph neural networks
ICLR 2021
Learning spectrograms with convolutional spectral kernels
AISTATS 2020
ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
NIPS 2019
Deep learning with differential Gaussian process flows
AISTATS 2019
Harmonizable mixture kernels with variational Fourier features
AISTATS 2019
Learning unknown ODE models with Gaussian processes
ICML 2018
Non-Stationary Spectral Kernels
NIPS 2017
A Mutually-Dependent Hadamard Kernel for Modelling Latent Variable Couplings
ACML 2017
Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo
AISTATS 2016
Random Fourier Features For Operator-Valued Kernels
ACML 2016