Search Results for author: Yilun Zhou

Found 12 papers, 7 papers with code

The Solvability of Interpretability Evaluation Metrics

no code implementations18 May 2022 Yilun Zhou, Julie Shah

Feature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency, which are motivated by the principle that more important features -- as judged by the explanation -- should have larger impacts on model prediction.

ExSum: From Local Explanations to Model Understanding

1 code implementation30 Apr 2022 Yilun Zhou, Marco Tulio Ribeiro, Julie Shah

Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment.

The Irrationality of Neural Rationale Models

1 code implementation14 Oct 2021 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.

Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment

no code implementations14 Feb 2021 Ganesh Ghalme, Vineet Nair, Vishakha Patil, Yilun Zhou

Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare.

Decision Making Fairness

Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

1 code implementation29 Dec 2020 Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, Asish Ghoshal

Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process.

Active Learning

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

Adversarially Guided Self-Play for Adopting Social Conventions

no code implementations16 Jan 2020 Mycal Tucker, Yilun Zhou, Julie Shah

Robotic agents must adopt existing social conventions in order to be effective teammates.

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

Learning Household Task Knowledge from WikiHow Descriptions

1 code implementation WS 2019 Yilun Zhou, Julie A. Shah, Steven Schockaert

Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment.

On Memory Mechanism in Multi-Agent Reinforcement Learning

no code implementations11 Sep 2019 Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah

Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.

Multi-agent Reinforcement Learning reinforcement-learning

Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness

1 code implementation21 Feb 2019 Yilun Zhou, Steven Schockaert, Julie A. Shah

In this paper we instead propose to learn to predict path quality from crowdsourced human assessments.

Knowledge Graphs

Cannot find the paper you are looking for? You can Submit a new open access paper.