Search Results for author: Serena Booth

Found 8 papers, 5 papers with code

Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example

1 code implementation19 Feb 2020 Serena Booth, Yilun Zhou, Ankit Shah, Julie Shah

To address these challenges, we introduce a flexible model inspection framework: Bayes-TrEx.

Domain Adaptation

Quality-Diversity Generative Sampling for Learning with Synthetic Data

1 code implementation22 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.

Fairness

The Irrationality of Neural Rationale Models

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.

Learning Optimal Advantage from Preferences and Mistaking it for Reward

1 code implementation3 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.

Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach

no code implementations9 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.

General Classification Probabilistic Programming

Models of human preference for learning reward functions

no code implementations5 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.

Decision Making reinforcement-learning

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