New York Conferences 2023 - International Conference on Machine Learning
September 1-3, 2023
New York City

Submission Guidelines

We accept files in .docx/.doc/.pdf/Latex Pdf format - as per the call for papers schedule.
Papers should be thoroughly checked and proofread before submission. After you have submitted your article you are unable to make any changes to it during the refereeing process—although if accepted, you will have a chance to make minor revisions after refereeing and before the final submission of your paper.
Formatting Guidelines

Paper content must be original and relevant to one of the many conference topics.
Authors are required to ensure accuracy of quotations, citations, diagrams, maps, and tables.
Figures and tables need to be placed where they are to appear in the text and must be clear and easy to view.
Papers must follow format according to the template which will be sent to accepted papers.
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