Search Results for author: Ben Lengerich

Found 6 papers, 4 papers with code

Data Science with LLMs and Interpretable Models

1 code implementation22 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.

Additive models Question Answering

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?

2 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 BIG-bench Machine Learning +3

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