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Generative / Discriminative Interface

Generative and Discriminative Learning Interface

Generative and discriminative learning are two of the major paradigms for solving prediction problems in machine learning, each offering important distinct advantages. They have often been studied in different sub-communities, but over the past decade, there has been increasing interest in trying to understand and leverage the advantages of both approaches. The goal of this workshop is to map out our current understanding of the empirical and theoretical advantages of each approach as well as their combination, and to identify open research directions.

The aim of this workshop is to provide a platform for both theoretical and applied researchers from different communities to discuss the status of our understanding on the interplay between generative and discriminative learning, as well as to identify forward-looking open problems of interest to the NIPS community. Examples of topics of interest to the workshop are as follows:

  • Theoretical analysis of generative vs. discriminative learning
  • Techniques for combining generative and discriminative approaches
  • Successful applications of hybrids
  • Empirical comparison of generative vs. discriminative learning
  • Inclusion of prior knowledge in discriminative methods (semi-supervised approaches, generalized expectation criteria, posterior regularization, etc.)
  • Insights into the role of generative/discriminative interface for deep learning
  • Computational issues in discriminatively trained generative models/hybrid models
  • Map of possible generative/discriminative approaches and combinations
  • Bayesian approaches optimized for predictive performance
  • Comparison of model-free and model-based approaches in statistics or reinforcement learning

The Workshop homepage can be found at


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