The proposed research is in the area of ``machine learning,` with an emphasis on the development of efficient algorithms of both practical and theoretical interest. The problems investigated include: (a) learning in the presence of noise (searching for learning linear-threshold and parity functions in the presence of classification noise, and `hypothesis boosting` is possible in the presence of classification noise); (b) exploration with obstacles (developing efficient algorithms for exploring planar regions with obstacles and/or planar graphs, with a view towards integrating these algorithms in larger navigation systems); (c) coordinating experts/feature selection (developing effective methods of combining the advice of a variety of experts/features for a given problem); (d) autonomous learning of causal structures (exploring how active learning can effectively learn a complex environment, such as a Macintosh window system); (e) new models of active learning (refining the understanding of the power and limitations of `membership queries` for learning); (f) learning and biology (looking at a variety of inference problems motivated by problems in molecular biology); and (g) teaching in different learning models (focusing on algorithms for `effective teaching`).
