G
Gustavo Lacerda
Researcher at Carnegie Mellon University
Publications - 11
Citations - 377
Gustavo Lacerda is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Cognitive model & Cognitive tutor. The author has an hindex of 8, co-authored 10 publications receiving 358 citations.
Papers
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Proceedings Article
Discovering cyclic causal models by independent components analysis
TL;DR: In this article, the authors generalized Shimizu et al.'s (2006) ICA-based approach for discovering linear non-Gaussian acyclic (LiNGAM) Structural Equation Models (SEMs) from causally sufficient, continuous-valued observational data.
Proceedings Article
Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer,Aapo Hyvärinen,Richard Scheines,Peter Spirtes,Joseph D. Ramsey,Gustavo Lacerda,Shohei Shimizu +6 more
TL;DR: This paper generalize and combine the two approaches to Independent Component Analysis, to yield a method able to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible.
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Causal discovery of linear acyclic models with arbitrary distributions
Patrik O. Hoyer,Aapo Hyvärinen,Richard Scheines,Peter Spirtes,Joseph D. Ramsey,Gustavo Lacerda,Shohei Shimizu +6 more
TL;DR: The authors combine conditional independencies and independent component analysis to learn the model structure in many cases for which the previous methods provide answers that are either incorrect or are not as informative as possible.
Proceedings Article
Predicting Students' Performance with SimStudent: Learning Cognitive Skills from Observation
TL;DR: A second use of SimStudent is evaluated, viz., student modeling for Intelligent Tutoring Systems, where the basic idea is to have SimStudent observe human students solving problems, and create a cognitive model that can replicate the students' performance.
Proceedings Article
Evaluating a Simulated Student Using Real Students Data for Training and Testing
TL;DR: SimStudent as discussed by the authors is a machine learning agent that learns cognitive skills by demonstration, which can then be used to model human students' performance as well and evaluate the applicability of SimStudent as a tool for modeling real students.