ICML
E99263
ICML (International Conference on Machine Learning) is one of the premier global academic conferences focused on research in machine learning and related fields.
All labels observed (12)
How this entity was disambiguated
This entity first appeared as the object of triple T805008 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ICML Context triple: [Wojciech Zaremba, publishedIn, ICML]
-
A.
ICLR
ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
-
B.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
C.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
D.
SIGKDD
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
-
E.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ICML Target entity description: ICML (International Conference on Machine Learning) is one of the premier global academic conferences focused on research in machine learning and related fields.
-
A.
ICLR
ICLR (International Conference on Learning Representations) is a leading annual machine learning conference focused on deep learning and representation learning research.
-
B.
NeurIPS
NeurIPS is a premier international conference focused on advances in machine learning, artificial intelligence, and computational neuroscience.
-
C.
IEEE International Conference on Data Mining
The IEEE International Conference on Data Mining is a leading annual research conference that focuses on advances in data mining, machine learning, and knowledge discovery in databases.
-
D.
SIGKDD
SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
-
E.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) is a premier annual international research conference showcasing cutting-edge advances in computer vision, machine learning, and pattern recognition.
- F. None of above. chosen
Statements (58)
| Predicate | Object |
|---|---|
| instanceOf |
academic conference
ⓘ
machine learning conference ⓘ |
| abbreviation | ICML ⓘ |
| audience |
academics
ⓘ
industry practitioners ⓘ researchers ⓘ |
| field |
artificial intelligence
ⓘ
data science ⓘ machine learning ⓘ statistics ⓘ |
| frequency | annual ⓘ |
| fullName |
International Conference on Machine Learning
ⓘ
linked to:
ICML
|
| hasEventType |
keynote talks
ⓘ
main conference ⓘ oral presentations ⓘ poster sessions ⓘ tutorials ⓘ workshops ⓘ |
| hasProceedings |
ICML conference proceedings
ⓘ
linked to:
ICML
|
| hasWebsite | https://icml.cc/ ⓘ |
| language | English ⓘ |
| organizedBy | International Machine Learning Society ⓘ |
| publicationType | archival conference proceedings ⓘ |
| ranking | top-tier machine learning conference ⓘ |
| reviewProcess | peer review ⓘ |
| scope | research in machine learning and related fields ⓘ |
| status | premier global conference in machine learning ⓘ |
| topic |
Bayesian methods
ⓘ
ML for scientific discovery ⓘ applied machine learning ⓘ bandit algorithms ⓘ causal inference in machine learning ⓘ deep learning ⓘ fairness in machine learning ⓘ generative models ⓘ graph neural networks ⓘ graphical models ⓘ interpretability of machine learning models ⓘ kernel methods ⓘ large-scale learning ⓘ meta-learning ⓘ multi-agent learning ⓘ online and streaming learning ⓘ online learning ⓘ optimization for machine learning ⓘ privacy in machine learning ⓘ probabilistic modeling ⓘ reinforcement learning ⓘ representation learning ⓘ robustness in machine learning ⓘ semi-supervised learning ⓘ structured prediction ⓘ supervised learning ⓘ theoretical machine learning ⓘ transfer learning ⓘ unsupervised learning ⓘ |
| typicalFormat | peer-reviewed conference ⓘ |
| typicalLocation | various international locations ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: ICML Description of subject: ICML (International Conference on Machine Learning) is one of the premier global academic conferences focused on research in machine learning and related fields.
Referenced by (48)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: ICML
linked to: ICML
linked to: ICML
Dueling DQN
→
citationVenue
→
Proceedings of the 33rd International Conference on Machine Learning
ⓘ
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML
Distributed Representations of Sentences and Documents
→
publishedIn
→
International Conference on Machine Learning
ⓘ
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML
Universal Value Function Approximators
→
publishedIn
→
International Conference on Machine Learning
ⓘ
linked to: ICML
linked to: ICML
Universal Value Function Approximators
→
citationVenue
→
Proceedings of the 32nd International Conference on Machine Learning
ⓘ
linked to: ICML
International Machine Learning Society
→
supportsEvent
→
International Conference on Machine Learning
ⓘ
linked to: ICML
International Machine Learning Society
→
overseesEvent
→
International Conference on Machine Learning
ⓘ
linked to: ICML
International Machine Learning Society
→
relatedConference
→
International Conference on Machine Learning
ⓘ
linked to: ICML
subject linked to:
Michael Jordan
linked to: ICML
subject linked to:
Michael Jordan
linked to: ICML
linked to: ICML
Connectionist Temporal Classification
→
publishedIn
→
Proceedings of the 23rd International Conference on Machine Learning
ⓘ
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML
linked to: ICML