Search Results for author: Ben Lengerich

Found 5 papers, 3 papers with code

Executive Function: A Contrastive Value Policy for Resampling and Relabeling Perceptions via Hindsight Summarization?

no code implementations27 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.

Continual Learning Few-Shot Learning +1

NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

1 code implementation1 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.

On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks

no code implementations28 Sep 2020 Ben Lengerich, Eric Xing, Rich Caruana

Conversely, the probability of an interaction of $k$ variables surviving Dropout at rate $p$ is $\mathcal{O}((1-p)^k)$.

How Interpretable and Trustworthy are GAMs?

3 code implementations11 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.

Additive models Inductive Bias +1

Neural Additive Models: Interpretable Machine Learning with Neural Nets

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.

Additive models Decision Making +2

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