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Nicholas Perello

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2 papers

1 author row

TMLR Journal 2024 Journal Article

Simple Steps to Success: A Method for Step-Based Counterfactual Explanations

Jenny Hamer
Nicholas Perello
Jason Valladares
Vignesh Viswanathan
Yair Zick

Algorithmic recourse is a process that leverages counterfactual explanations, going beyond understanding why a system produced a given classification, to providing a user with actions they can take to change their predicted outcome. Existing approaches to compute such interventions---known as recourse---identify a set of points that satisfy some desiderata---e.g. an intervention in the underlying causal graph, minimizing a cost function, etc. Satisfying these criteria, however, requires extensive knowledge of the underlying model structure, an often unrealistic amount of information in several domains. We propose a data-driven and model-agnostic framework to compute counterfactual explanations. We introduce StEP, a computationally efficient method that offers incremental steps along the data manifold that directs users towards their desired outcome. We show that StEP uniquely satisfies a desirable set of axioms. Furthermore, via a thorough empirical and theoretical investigation, we show that StEP offers provable robustness and privacy guarantees while outperforming popular methods along important metrics.

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AAAI Conference 2022 Conference Paper

On Optimizing Interventions in Shared Autonomy

Weihao Tan
David Koleczek
Siddhant Pradhan
Nicholas Perello
Vivek Chettiar
Vishal Rohra
Aaslesha Rajaram
Soundararajan Srinivasan

Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performance, it may often also be beneficial that the agent concurrently accounts for preserving the user’s experience or satisfaction of collaboration. In order to address this additional goal, we examine approaches for improving the user experience by constraining the number of interventions by the autonomous agent. We propose two model-free reinforcement learning methods that can account for both hard and soft constraints on the number of interventions. We show that not only does our method outperform the existing baseline, but also eliminates the need to manually tune a black-box hyperparameter for controlling the level of assistance. We also provide an in-depth analysis of intervention scenarios in order to further illuminate system understanding.

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