Rational Approximations to Rational Models of Categorization

Can statistical machine learning theories and algorithms help explain human learning? Broadly speaking, machine learning studies the fundamental laws that govern all learning processes, including both artificial systems (e.g., computers) and natural systems (e.g., humans). It has long been understood that theories and algorithms from machine learning are relevant to understanding aspects of human learning. Human cognition also carries potential lessons for machine learning research, since people still learn languages, concepts, and causal relationships from far less data than any automated system. There is a rich opportunity to develop a general theory of learning which covers both machines and humans, with the potential to deepen our understanding of human cognition and to take insights from human learning to improve machine learning systems. The goal of this workshop is to bring together the different communities that study machine learning, cognitive science, neuroscience and educational science. We will investigate the value of advanced machine learning theories and algorithms as computational models for certain human learning behaviors, including, but not limited to, the role of prior knowledge, learning from labeled and unlabeled data, learning from active queries, and so on. We also wish to explore the insights from the cognitive study of human learning to inspire novel machine learning theories and algorithms. It is our hope that the NIPS workshop will provide a venue for cross-pollination of machine learning approaches and cognitive theories of learning to spur further advances in both areas. 7.30-7.35 Welcome Workshop Organizers 7.35-7.55 Training Deep Architectures: Inspiration from Humans Yoshua Bengio 7.55-8.15 Stochastic programs as a framework for clustering and causation Noah Goodman 8.15-8.35 Learning abstract causal knowledge: a case study in human and machine learning Josh Tenenbaum 8.35-8.55 Rational Approximations to Rational Models of Categorization Adam Sanborn