146 citations · 517 across the 26 of their papers we have counts for
26 papers
Foundation Posteriors for Approximate Probabilistic Inference
Mike Wu, Noah Goodman
Probabilistic programs provide an expressive representation language for generative models. Given a probabilistic program, we are interested in the task of posterior inference: est…
Know Thy Student: Interactive Learning with Gaussian Processes
Rose E. Wang, Mike Wu, Noah Goodman
Learning often involves interaction between multiple agents. Human teacher-student settings best illustrate how interactions result in efficient knowledge passing where the teacher…
Tradeoffs Between Contrastive and Supervised Learning: An Empirical Study
Ananya Karthik, Mike Wu, Noah Goodman +1
Contrastive learning has made considerable progress in computer vision, outperforming supervised pretraining on a range of downstream datasets. However, is contrastive learning the…
Temperature as Uncertainty in Contrastive Learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti +1
Contrastive learning has demonstrated great capability to learn representations without annotations, even outperforming supervised baselines. However, it still lacks important prop…
Modeling Item Response Theory with Stochastic Variational Inference
Mike Wu, Richard L. Davis, Benjamin W. Domingue +2
Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to…
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
Mike Wu, Noah Goodman, Chris Piech +1
High-quality computer science education is limited by the difficulty of providing instructor feedback to students at scale. While this feedback could in principle be automated, sup…