International Conference on Machine Learning information
Academic conference in machine learning
Part of a series on
Machine learning and data mining
Paradigms
Supervised learning
Unsupervised learning
Online learning
Batch learning
Meta-learning
Semi-supervised learning
Self-supervised learning
Reinforcement learning
Curriculum learning
Rule-based learning
Quantum machine learning
Problems
Classification
Generative modeling
Regression
Clustering
Dimensionality reduction
Density estimation
Anomaly detection
Data cleaning
AutoML
Association rules
Semantic analysis
Structured prediction
Feature engineering
Feature learning
Learning to rank
Grammar induction
Ontology learning
Multimodal learning
Supervised learning (classification • regression)
Apprenticeship learning
Decision trees
Ensembles
Bagging
Boosting
Random forest
k-NN
Linear regression
Naive Bayes
Artificial neural networks
Logistic regression
Perceptron
Relevance vector machine (RVM)
Support vector machine (SVM)
Clustering
BIRCH
CURE
Hierarchical
k-means
Fuzzy
Expectation–maximization (EM)
DBSCAN
OPTICS
Mean shift
Dimensionality reduction
Factor analysis
CCA
ICA
LDA
NMF
PCA
PGD
t-SNE
SDL
Structured prediction
Graphical models
Bayes net
Conditional random field
Hidden Markov
Anomaly detection
RANSAC
k-NN
Local outlier factor
Isolation forest
Artificial neural network
Autoencoder
Cognitive computing
Deep learning
DeepDream
Feedforward neural network
Kolmogorov–Arnold Network
Recurrent neural network
LSTM
GRU
ESN
reservoir computing
Restricted Boltzmann machine
GAN
Diffusion model
SOM
Convolutional neural network
U-Net
Transformer
Vision
Mamba
Spiking neural network
Memtransistor
Electrochemical RAM (ECRAM)
Reinforcement learning
Q-learning
SARSA
Temporal difference (TD)
Multi-agent
Self-play
Learning with humans
Active learning
Crowdsourcing
Human-in-the-loop
RLHF
Model diagnostics
Coefficient of determination
Confusion matrix
Learning curve
ROC curve
Mathematical foundations
Kernel machines
Bias–variance tradeoff
Computational learning theory
Empirical risk minimization
Occam learning
PAC learning
Statistical learning
VC theory
Machine-learning venues
ECML PKDD
NeurIPS
ICML
ICLR
IJCAI
ML
JMLR
Related articles
Glossary of artificial intelligence
List of datasets for machine-learning research
List of datasets in computer vision and image processing
Outline of machine learning
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The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.[1] It is supported by the International Machine Learning Society (IMLS). Precise dates vary year to year, but paper submissions are generally due at the end of January, and the conference is generally held the following July. The first ICML was held 1980 in Pittsburgh.[2][3]
^"Artificial Intelligence - Google Scholar Metrics". 2020-10-07. Archived from the original on 2020-10-07. Retrieved 2020-10-07.
^"Past ICMLs Online Proceedings". University of Trier. Aktuálně.cz. 1 June 1980. Archived from the original on 2008-12-12. Retrieved 31 January 2023.
^"ICML 2008, Helsinki, Finland". University of Trier. 1 June 1980. Retrieved 31 January 2023.
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