SLMC //research interests//
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Research Interests
 The SLMC group has a broad and highly inter-disciplinary research agenda spanning statistical machine learning, formal learning theory, learning in connectionist systems (artificial and biological); robotic, humanoid and biological motor control, adaptive/learning control; and multimodal cue integration and attentional strategies.
Software
 Some of our work has resulted in software packages that may be useful to other researchers or practitioners.
 
Publications
 An up-to-date publication list can be found here.

Some of the topics of recent interest are listed below (with links to papers).

bullet Statistical Learning

  • Nonparametric Learning in High Dimensions (papers)
  • Bayesian Approaches (papers)
  • Kernel and Exact Incremental methods- Functional Analysis (papers)
  • Active/Query based learning & Learning curves (papers)
  • Reinforcement Learning (papers)
bullet Visual Attention & Oculomotor Control (papers)
bullet Real Time Learning for Robot Control & High Dimensional Systems (papers)
bullet Dimensionality Reduction (papers)
 bullet Multimodal Learning and Cue Integration
bullet Haptic Discrimination: Encoding and Decoding Strategies