Jakob Runge
21 papers · 2018–2026 · 8 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+5 more ↓ Show less ↑
🌉 Interdisciplinary Bridge · Silver 🌍 Conference Polyglot · Silver 🏃 Academic Marathon · Sprint 🐣 Hot Topic Early Bird 🗺️ Taxonomy Completionist (21)
🔬
Deep Specialist
(13)
💎
Century Club · Bronze
⚡
Prolific Year · Productive
🗃️
Keyword Collector
(76)
🔥
Unstoppable · Bronze
Top co-authors
Research topics
Machine Learning
(15)
Artificial Intelligence
(13)
Core AI
(13)
Learning Types · Machine Learning
(7)
Knowledge & Reasoning
(6)
Reasoning
(6)
Data Science & Analytics
(5)
Core Methods
(5)
Mathematics & Optimization
(4)
Mathematics
(4)
Methods
(4)
Deep Learning
(2)
Application Areas
(2)
Bayesian & Probabilistic
(2)
Learning Types · Deep Learning
(2)
Keywords
causal discovery
(13)
time series
(6)
structural causal model
(5)
conditional independence
(4)
conditional independence test
(3)
causal graph
(3)
causal representation learning
(3)
causal inference
(3)
graphical model
(3)
non-parametric method
(2)
conditional independence testing
(2)
conditional mutual information
(1)
partial correlation
(1)
machine learning
(1)
causal effect estimation
(1)
benchmark evaluation
(1)
graph embedding
(1)
mutual information
(1)
latent variable model
(1)
k-nearest neighbor
(1)
Papers
How PC-based Methods Err: Towards Better Reporting of Assumption Violations and Small Sample Errors
UAI 2026
Learning a Spatial Partitioning and its Causal Relations from Temporal Data
CLeaR 2026
Non-parametric Conditional Independence Testing for Mixed Continuous-Categorical Variables: A Novel Method and Numerical Evaluation
CLeaR 2025
Sanity Checking Causal Representation Learning on a Simple Real-World System
ICML 2025
Separation-Based Distance Measures for Causal Graphs
AISTATS 2025
Unitless Unrestricted Markov-Consistent SCM Generation: Better Benchmark Datasets for Causal Discovery
CLeaR 2025
A Global Markov Property for Solutions of Stochastic Difference Equations and the corresponding Full Time Graphs
UAI 2024
Bootstrap aggregation and confidence measures to improve time series causal discovery
CLeaR 2024
Causal discovery with endogenous context variables
NeurIPS 2024
Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions
CLeaR 2024
Causal Discovery for time series from multiple datasets with latent contexts
UAI 2023
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
NeurIPS 2023
Distinguishing Cause and Effect in Bivariate Structural Causal Models: A Systematic Investigation
JMLR 2023
Increasing effect sizes of pairwise conditional independence tests between random vectors
UAI 2023
Vector Causal Inference between Two Groups of Variables
AAAI 2023
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery
NeurIPS 2022
Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables
NeurIPS 2021
Determining the Relevance of Features for Deep Neural Networks
ECCV 2020
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
UAI 2020
High-recall causal discovery for autocorrelated time series with latent confounders
NeurIPS 2020
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
AISTATS 2018