Teaching a generative model: Mathematical formulation and solution framework

Honggang Wang, Bo Zhang

Research output: Contribution to journalArticlepeer-review


We develop the mathematical formulation for teaching generative models to a learner whose learning processes and cognitive behaviors may be analytically intractable, but can be simulated by numerical processes. The model considers the learner's bias (prior knowledge) or memory process by using stochastic models. We also present an optimization framework for solving the involved non-convex, stochastic optimization problems associated with machine teaching. The algorithm design and the conditions and analysis are discussed for local convergence properties of the proposed optimization algorithms. In the paper, we discuss a number of example cases to illustrate the algorithmic ideas and demonstrate their efficiency.

Original languageEnglish (US)
Pages (from-to)119-126
Number of pages8
JournalComputers and Industrial Engineering
StatePublished - Apr 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)


  • Computer simulation
  • Data driven optimization
  • Machine teaching
  • Numerical algorithms
  • Statistical learning

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