8 citations · 8 across the 1 of their papers we have counts for
3 papers
cs.LG2021
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt +3
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets. Such models aim to incorporate pre-specified, high-level concepts into the learning pr…
stat.ML2020
Neural Approximate Sufficient Statistics for Implicit Models
Yanzhi Chen, Dinghuai Zhang, Michael Gutmann +2
We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractab…
stat.CO2019★ 8 cited
Adaptive Gaussian Copula ABC
Yanzhi Chen, Michael U. Gutmann
Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents…