Long Ouyang
Long Ouyang
Department of Psychology, Stanford University
Verified email at stanford.edu
Cited by
Cited by
Practical optimal experiment design with probabilistic programs
L Ouyang, MH Tessler, D Ly, N Goodman
arXiv preprint arXiv:1608.05046, 2016
Fabular: Regression formulas as probabilistic programming
J Borgström, AD Gordon, L Ouyang, C Russo, A Ścibior, M Szymczak
Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of …, 2016
Semantic coherence facilitates distributional learning
L Ouyang, L Boroditsky, MC Frank
Cognitive science 41, 855-884, 2017
Learning to summarize with human feedback
N Stiennon, L Ouyang, J Wu, D Ziegler, R Lowe, C Voss, A Radford, ...
Advances in Neural Information Processing Systems 33, 2020
webppl-oed: A practical optimal experiment design system.
L Ouyang, MH Tessler, D Ly, ND Goodman
CogSci, 2018
Semantic Coherence Facilitates Distributional Learning of Word Meanings
L Ouyang, L Boroditsky, MC Frank
Proceedings of the 34th Annual Meeting of the Cognitive Science Society, 2012
Pedagogical learning
L Ouyang, MC Frank
arXiv preprint arXiv:1711.09401, 2017
Bayesian Inference of Regular Expressions from Human-Generated Example Strings
L Ouyang
arXiv preprint arXiv:1805.08427, 2018
The Effect of Learning on Learning
L Ouyang
Stanford University, 2015
Support and influence analysis for visualizing posteriors of probabilistic programs
L Ouyang
The system can't perform the operation now. Try again later.
Articles 1–10