Institute for Mathematical Behavioral Sciences
CONFERENCE ON “ HUMAN AND MACHINE LEARNING”
March 13-15, 2009
ABSTRACTS and PAPERS
WILLIAM H. BATCHELDER, Cognitive Sciences, UC Irvine
"Learning Theory: History, Formalisms, and Perennial Issues"
LI DENG, Speech Research Group,
Microsoft Research
“Acoustic Modeling in Automatic Speech Recognition Overview of Current State and Research Challenges ”
JEAN-CLAUDE FALMAGNE, Cognitive Sciences,
UC Irvine
“Learning Spaces--Concepts, Results, Applications”
TOM GRIFFITHS, Department of Psychology, UC Berkeley
“Connecting human and machine learning via probabilistic models of cognition ”
"Analyzing human feature learning as non-parametric Bayesian influence"
"Markov chain Monte Carlo with people"
"Categorization as nonparametric Bayesian Density Estimation"
TONY JEBARA, Computer Science,
Columbia University
"Learning Networks of Places and People from Location Data”
(Abstract) (Algorithm Paper)
MICHAEL JORDAN, EECS, Statistics, UC Berkeley
“Combinatorial Stochastic Processes and Nonparametric Bayesian Modeling”
"Shared segmentation of natural scenes using dependent Pitman-Yor processes"
"Hierarchal Bayesian nonparametric models with applications"
MICHAEL LITTMAN, Computer Science, Rutgers
“Initial explorations of cognitive reinforcement learning”
DeLIANG WANG, Computer Science and Engineering, Ohio State University
“Cocktail Party Processing”