Machine Learning (Theory)


Learning Research Programs

Tags: Organization jl@ 5:56 pm

This is an attempt to organize the broad research programs related to machine learning currently underway. This isn’t easy—this map is partial, the categories often overlap, and there are many details left out. Nevertheless, it is (perhaps) helpful to have some map of what is happening where. The word ‘typical’ should not be construed narrowly here.

  1. Learning Theory Focuses on analyzing mathematical models of learning, essentially no experiments. Typical conference: COLT.
  2. Bayesian Learning Bayes law is always used. Focus on methods of speeding up or approximating integration, new probabilistic models, and practical applications. Typical conferences: NIPS,UAI
  3. Structured learning Predicting complex structured outputs, some applications. Typiical conferences: NIPS, UAI, others
  4. Reinforcement Learning Focused on ‘agent-in-the-world’ learning problems where the goal is optimizing reward. Typical conferences: ICML
  5. Unsupervised Learning/Clustering/Dimensionality Reduction Focused on simpiflying data. Typicaly conferences: Many (each with a somewhat different viewpoint)
  6. Applied Learning Worries about cost sensitive learning, what to do on very large datasets, applications, etc.. Typical conference: KDD
  7. Supervised Leanring Chief concern is making practical algorithms for simpler predictions. Many applications. Typical conference: ICML

Please comment on any missing pieces—it would be good to build up a better understanding of what are the focuses and where they are.

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