Categories |
MACHINE LEARNING
COMPUTER SCIENCE
ML
AI & ML
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Call for Papers |
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Topics of interest for submission include but are not limited to:
Machine learning Supervised learning Ranking Supervised learning by classification Supervised learning by regression Structured outputs Cost-sensitive learning Unsupervised learning Cluster analysis Anomaly detection Mixture modeling Topic modeling Source separation Motif discovery Dimensionality reduction and manifold learning Reinforcement learning Sequential decision making Inverse reinforcement learning Apprenticeship learning Multi-agent reinforcement learning Adversarial learning Multi-task learning Transfer learning Lifelong machine learning Learning under covariate shift Learning settings Batch learning Online learning settings Learning from demonstrations Learning from critiques Learning from implicit feedback Active learning settings Semi-supervised learning settings Machine learning approaches Classification and regression trees Kernel methods Support vector machines Gaussian processes Neural networks Logical and relational learning Inductive logic learning Statistical relational learning Learning in probabilistic graphical models Maximum likelihood modeling Maximum entropy modeling Maximum a posteriori modeling Mixture models Latent variable models Bayesian network models Learning linear models Perceptron algorithm Factorization methods Non-negative matrix factorization Factor analysis Principal component analysis Canonical correlation analysis Latent Dirichlet allocation Rule learning Instance-based learning Markov decision processes Partially-observable Markov decision processes Stochastic games Learning latent representations Deep belief networks Bio-inspired approaches Artificial life Evolvable hardware Genetic algorithms Genetic programming Evolutionary robotics Generative and developmental approaches Machine learning algorithms Dynamic programming for Markov decision processes Value iteration Q-learning Policy iteration Temporal difference learning Approximate dynamic programming methods Ensemble methods Boosting Bagging Spectral methods Feature selection Regularization Cross-validation |
Summary |
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NYC-2024-ML 2024 : New York Annual Conference on Machine Learning 2024 will take place in New York, USA. It’s a 3 days event starting on Jun 27, 2024 (Thursday) and will be winded up on Jun 29, 2024 (Saturday). NYC-2024-ML 2024 falls under the following areas: MACHINE LEARNING, COMPUTER SCIENCE, ML, AI & ML, etc. Submissions for this Conference can be made by Jan 10, 2024. Authors can expect the result of submission by Jan 30, 2024. Upon acceptance, authors should submit the final version of the manuscript on or before Apr 30, 2024 to the official website of the Conference. Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines. Other Details of the NYC-2024-ML 2024
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Credits and Sources |
| [1] NYC-2024-ML 2024 : New York Annual Conference on Machine Learning 2024 |
