1 code implementation • 22 Feb 2024 • Sebastian Bordt, Ben Lengerich, Harsha Nori, Rich Caruana
Recent years have seen important advances in the building of interpretable models, machine learning models that are designed to be easily understood by humans.
no code implementations • 27 Apr 2022 • Chris Lengerich, Ben Lengerich
We develop the few-shot continual learning task from first principles and hypothesize an evolutionary motivation and mechanism of action for executive function as a contrastive value policy which resamples and relabels perception data via hindsight summarization to minimize attended prediction error, similar to an online prompt engineering problem.
1 code implementation • 1 Nov 2021 • Ben Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis
We encode the acyclicity constraint as a smooth regularization loss which is back-propagated to the mixing function; in this way, NOTMAD shares information between context-specific acyclic graphs, enabling the estimation of Bayesian network structures and parameters at even single-sample resolution.
no code implementations • 28 Sep 2020 • Ben Lengerich, Eric Xing, Rich Caruana
Conversely, the probability of an interaction of $k$ variables surviving Dropout at rate $p$ is $\mathcal{O}((1-p)^k)$.
2 code implementations • 11 Jun 2020 • Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana
Generalized additive models (GAMs) have become a leading modelclass for interpretable machine learning.
6 code implementations • NeurIPS 2021 • Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, Geoffrey Hinton
They perform similarly to existing state-of-the-art generalized additive models in accuracy, but are more flexible because they are based on neural nets instead of boosted trees.