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Call For Papers

The ICPAPML bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Probability Theory, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Probabilistic models in machine learning
  • Bayesian methods for machine learning
  • Stochastic processes in AI applications
  • Probabilistic graphical models in ML
  • Uncertainty quantification in machine learning
  • Applications of Bayesian networks
  • Probabilistic approaches to deep learning
  • Statistical learning theory and applications
  • Reinforcement learning with probabilistic models
  • Probabilistic methods for natural language processing
  • Machine learning for predictive analytics
  • Ensemble methods in probabilistic learning
  • Probabilistic models for time series analysis
  • Applications of Markov models in ML
  • Probabilistic reasoning in AI systems
  • Statistical methods for model evaluation
  • Machine learning with incomplete data
  • Probabilistic approaches to computer vision
  • Applications of probabilistic models in healthcare
  • Probabilistic methods for anomaly detection

Evaluation

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.