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NYC-2024-ML 2024 : New York Annual Conference on Machine Learning 2024


When Jun 27, 2024 - Jun 29, 2024
Where New York, USA
Submission Deadline Feb 28, 2024
Notification Due Mar 10, 2024
Final Version Due Apr 25, 2024
Categories    machine learning   supervised learning   unsupervised learning   reinforcement learning

Call For Papers

Topics of interest for submission include but are not limited to:
Machine learning

Supervised learning
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
Policy iteration
Temporal difference learning
Approximate dynamic programming methods
Ensemble methods
Spectral methods
Feature selection

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