CONALDConference on Automated Learning and Discovery
References in periodicals archive ?
CONALD featured seven plenary talks, given by (1) Tom Dietterich (Oregon State University),"Learning for Sequential Decision Making"; (2) Stuart Geman (Brown University), "Probabilistic Grammars and Their Applications"; (3) David Heckerman (Microsoft Research), "A Bayesian Approach to Causal Discovery"; (4) Michael Jordan (Massachusetts Institute of Technology, now at the University of California at Berkeley), "Graphical Models and Variational Approximation"; (5) Daryl Pregibon (AT&T Research), "Real-Time Learning and Discovery in Large-Scale Networks"; (6) Herbert Simon (CMU), "Using Machine Learning to Understand Human Learning"; and (7) Robert Tibshirani (University of Toronto, now at Stanford University), "Learning from Data: Statistical Advances and Challenges.
A key objective of the CONALD meeting was to investigate the role of a cross-disciplinary approach.
This list is based on the outcomes of the individual workshops, the invited talks, and various discussions that occurred in the context of CONALD.
We believe that CONALD has successfully contributed to an ongoing dialogue between different disciplines that, for a long time, have studied different facets of one and the same problem: decision making based on historical data.