EDBT 2026 Demo / reviewers in the wild / expert
Magnus Stensmo
dblp:97/6035
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2002
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-author
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 · 43% Multi-agent systems · 28% Reinforcement learning · 28% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Medical and health informatics · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning › imitation learning
inverse reinforcement learning |
0.0 | 1 | 1996 | Learning Decision Theoretic Utilities through Reinforcement Learning · NIPS 1996 |
Knowledge, reasoning and agents › Multi-agent systems › multi-agent learning
utility learning |
0.0 | 1 | 1996 | Learning Decision Theoretic Utilities through Reinforcement Learning · NIPS 1996 |
Machine learning › Probabilistic and Bayesian machine learning › structured models › latent variable model
mixture model |
0.0 | 1 | 1994 | A Mixture Model System for Medical and Machine Diagnosis · NIPS 1994 |
Medical and health informatics
clinical diagnosis |
0.0 | 1 | 1994 | A Mixture Model System for Medical and Machine Diagnosis · NIPS 1994 |
Methods — techniques the papers use, named apart from their topics
mixture model · 0.0maximum utility principle · 0.0expectation-maximization · 0.0reinforcement learning · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | A Scalable and Efficient Probabilistic Information Retrieval and Text Mining System
Magnus Stensmo |
ICANN | 1 |
| 1996 | Learning Decision Theoretic Utilities through Reinforcement Learning
Magnus Stensmo, Terrence J. Sejnowski |
NIPS | 1 |
| 1994 | A Mixture Model System for Medical and Machine DiagnosisabstractDiagnosis of human disease or machine fault is a missing data problem since many variables are initially unknown. Additional information needs to be obtained. The j oint probability distribution of the data can be used to solve this problem. We model this with mixture models whose parameters are estimated by the EM algorithm. This gives the benefit that missing data in the database itself can also be handled correctly. The request for new information to refine the diagnosis is performed using the maximum utility principle. Since the system is based on learning it is domain independent and less labor intensive than expert systems or probabilistic networks. An example using a heart disease database is presented. Magnus Stensmo, Terrence J. Sejnowski |
NIPS | 1 |