activity
20222024
most citedRisk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning

4 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2024

Guarantee Regions for Local Explanations

Marton Havasi, Sonali Parbhoo, Finale Doshi-Velez

Interpretability methods that utilise local surrogate models (e.g. LIME) are very good at describing the behaviour of the predictive model at a point of interest, but they are not…

cs.CR2023

Adaptive Experimental Design for Intrusion Data Collection

Kate Highnam, Zach Hanif, Ellie Van Vogt +3

Intrusion research frequently collects data on attack techniques currently employed and their potential symptoms. This includes deploying honeypots, logging events from existing de…

cs.LG2023

Bayesian Inverse Transition Learning for Offline Settings

Leo Benac, Sonali Parbhoo, Finale Doshi-Velez

Offline Reinforcement learning is commonly used for sequential decision-making in domains such as healthcare and education, where the rewards are known and the transition dynamics…

cs.LG2023

Leveraging Factored Action Spaces for Off-Policy Evaluation

Aaman Rebello, Shengpu Tang, Jenna Wiens +1

Off-policy evaluation (OPE) aims to estimate the benefit of following a counterfactual sequence of actions, given data collected from executed sequences. However, existing OPE esti…

cs.LG20234 cited

Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning

Taylor W. Killian, Sonali Parbhoo, Marzyeh Ghassemi

In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These s…

cs.LG2022

Policy Optimization with Sparse Global Contrastive Explanations

Jiayu Yao, Sonali Parbhoo, Weiwei Pan +1

We develop a Reinforcement Learning (RL) framework for improving an existing behavior policy via sparse, user-interpretable changes. Our goal is to make minimal changes while gaini…