1 code implementation • 27 Apr 2021 • Yilun Zhou, Serena Booth, Marco Tulio Ribeiro, Julie Shah
Feature attribution methods are exceedingly popular in interpretable machine learning.
1 code implementation • 19 Feb 2020 • Serena Booth, Yilun Zhou, Ankit Shah, Julie Shah
To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx.
1 code implementation • 22 Dec 2023 • Allen Chang, Matthew C. Fontaine, Serena Booth, Maja J. Matarić, Stefanos Nikolaidis
QDGS is a model-agnostic framework that uses prompt guidance to optimize a quality objective across measures of diversity for synthetically generated data, without fine-tuning the generative model.
1 code implementation • NAACL (TrustNLP) 2022 • Yiming Zheng, Serena Booth, Julie Shah, Yilun Zhou
We call for more rigorous and comprehensive evaluations of these models to ensure desired properties of interpretability are indeed achieved.
1 code implementation • 3 Oct 2023 • W. Bradley Knox, Stephane Hatgis-Kessell, Sigurdur Orn Adalgeirsson, Serena Booth, Anca Dragan, Peter Stone, Scott Niekum
Most recent work assumes that human preferences are generated based only upon the reward accrued within those segments, or their partial return.
no code implementations • 9 Jan 2020 • Serena Booth, Ankit Shah, Yilun Zhou, Julie Shah
In this paper, we consider the problem of exploring the prediction level sets of a classifier using probabilistic programming.
no code implementations • 6 Oct 2021 • Aspen Hopkins, Serena Booth
Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods.
no code implementations • 5 Jun 2022 • W. Bradley Knox, Stephane Hatgis-Kessell, Serena Booth, Scott Niekum, Peter Stone, Alessandro Allievi
We empirically show that our proposed regret preference model outperforms the partial return preference model with finite training data in otherwise the same setting.