EDBT 2026 Demo / reviewers in the wild / expert
Kenneth Bollen
dblp:54/8261
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Probabilistic and Bayesian machine learning · 88% 3D vision · 12% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Probabilistic and Bayesian machine learning › causal inference
causal discovery |
0.3 | 2 | 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions · J. Mach. Learn. Res. 2014 DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model · J. Mach. Learn. Res. 2011 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference |
0.2 | 1 | 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions · J. Mach. Learn. Res. 2014 |
Machine learning › Probabilistic and Bayesian machine learning › causal inference › causal model
structural equation models |
0.2 | 1 | 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions · J. Mach. Learn. Res. 2014 |
Computer vision › 3D vision › structure from motion
direct method |
0.1 | 1 | 2011 | DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model · J. Mach. Learn. Res. 2011 |
Machine learning › Probabilistic and Bayesian machine learning › causal inference › causal discovery
linear non-gaussian acyclic model |
0.1 | 1 | 2011 | DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model · J. Mach. Learn. Res. 2011 |
Machine learning › Probabilistic and Bayesian machine learning
causal inference |
0.1 | 1 | 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions · J. Mach. Learn. Res. 2014 |
Machine learning › Probabilistic and Bayesian machine learning › causal inference
deconfounding |
0.1 | 1 | 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions · J. Mach. Learn. Res. 2014 |
Methods — techniques the papers use, named apart from their topics
non-gaussianity · 0.2bayesian estimation · 0.2independent component analysis · 0.1direct estimation · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Combining Graphical and Algebraic Approaches for Parameter Identification in Latent Variable Structural Equation ModelsabstractMeasurement error is ubiquitous in many variables “latent-to-observed” (L2O) transformation from the MIIV approach and develop an equivalent graphical L2O transformation that allows applying existing graphical criteria to latent parameters in SEMs. We combine L2O transformation with graphical instrumental variable criteria to obtain an efficient algorithm for non-iterative parameter identification in SEMs with latent variables. We prove that this graphical L2O transformation with the instrumental set criterion is equivalent to the state-of-the-art MIIV approach for SEMs, and show that it can lead to novel identification strategies when combined with other graphical criteria. Ankur Ankan, Inge M. N. Wortel, Kenneth Bollen, Johannes Textor |
AISTATS | 3 |
| 2014 | Bayesian estimation of causal direction in acyclic structural equation models with individual-specific confounder variables and non-Gaussian distributions
Shohei Shimizu, Kenneth Bollen |
J. Mach. Learn. Res. | 2 |
| 2011 | DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen |
J. Mach. Learn. Res. | 8 |