Jes Frellsen
20 papers · 2016–2025 · 7 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (30) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Topic Evolution
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Triple Crown
🏆
Keyword Champion
(4)
🚀
Conference Pioneer
🔥
Unstoppable · Bronze
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The Questioner
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Prolific Year · Productive
💎
Century Club · Bronze
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Keyword Collector
(97)
Top co-authors
Research topics
Machine Learning
(15)
Deep Learning
(11)
Core Methods
(9)
Models
(9)
Optimization & Theory
(9)
Artificial Intelligence
(5)
Learning Types
(4)
Mathematics & Optimization
(3)
Bayesian & Probabilistic · Machine Learning
(3)
Core AI
(3)
Application Areas
(2)
Architectures
(2)
Bayesian & Probabilistic · Artificial Intelligence
(2)
Mathematics
(2)
Computer Vision
(1)
Keywords
deep latent variable model
(4)
generative model
(4)
gaussian process
(3)
missing data
(3)
variational inference
(3)
density estimation
(2)
markov chain monte carlo
(2)
bayesian inference
(2)
cholesky decomposition
(2)
out-of-distribution detection
(2)
ising model
(1)
feature importance
(1)
maximum entropy
(1)
supervised learning
(1)
model calibration
(1)
ensemble learning
(1)
gibbs sampling
(1)
exact inference
(1)
hierarchical model
(1)
uncertainty quantification
(1)
Papers
Bayesian Circular Regression with von Mises Quasi-Processes
AISTATS 2025
Hyper-Transforming Latent Diffusion Models
ICML 2025
Kinetic Langevin Diffusion for Crystalline Materials Generation
ICML 2025
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
MICCAI 2024
Adaptive Cholesky Gaussian Processes
AISTATS 2023
Explainability as statistical inference
ICML 2023
Implicit Variational Inference for High-Dimensional Posteriors
NeurIPS 2023
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
JMLR 2023
That Label's got Style: Handling Label Style Bias for Uncertain Image Segmentation
ICLR 2023
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity
EMNLP 2022
How to deal with missing data in supervised deep learning?
ICLR 2022
Model-agnostic out-of-distribution detection using combined statistical tests
AISTATS 2022
Bounds all around: training energy-based models with bidirectional bounds
NeurIPS 2021
Hierarchical VAEs Know What They Don’t Know
ICML 2021
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
ICLR 2021
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
ICML 2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
ICML 2019
Leveraging the Exact Likelihood of Deep Latent Variable Models
NeurIPS 2018
Spherical convolutions and their application in molecular modelling
NeurIPS 2017
Bayesian Generalised Ensemble Markov Chain Monte Carlo
AISTATS 2016