International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS)

Description

The International Conference on Frontiers of Artificial Intelligence and Statistics (CFAIS) covers topics such as:

  • Algorithms and architectures for high-performance computation in AI and statistics
  • Solicited topics include, but are not limited to:
  • Bayesian models and estimation (graphical models, causality, Gaussian processes, approximate inference...)
  • Algorithms and architectures for high-performance computation in AI and statistics
  • Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models..
  • Classification, regression, density estimation, unsupervised and semi-supervised learning, clustering, topic models..
  • Game theory, no-regret learning, multi-agent systems
  • Deep learning including optimization, generalization and architectures
  • Models and estimation: graphical models, causality, Gaussian processes, approximate inference, kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing..
  • Game theory, no-regret learning, multi-agent systems
  • Reinforcement learning, planning, control
  • Non-Bayesian models and estimation (kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing...)
  • Software for and applications of AI and statistics
  • Reinforcement learning, planning, control
  • Structured prediction, relational learning, logic and probability
  • Software for and applications of AI and statistics
  • Trustworthy learning, including learning with privacy and fairness, interpretability, and robustness
  • Structured prediction, relational learning, logic and probability

Future Events

Past Events

Important

Please, check the official conference website for possible changes, before you make any traveling arrangements

Prices are for evaluation only.

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