20 citations · 70 across the 17 of their papers we have counts for
17 papers
Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques
Daking Rai, Bailin Wang, Yilun Zhou +1
Compositional and domain generalization present significant challenges in semantic parsing, even for state-of-the-art semantic parsers based on pre-trained language models (LMs). I…
Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks
Yilun Zhou
Counterfactual (CF) explanations, also known as contrastive explanations and algorithmic recourses, are popular for explaining machine learning models in high-stakes domains. For a…
Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)
Daking Rai, Yilun Zhou, Bailin Wang +1
While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying…
The Solvability of Interpretability Evaluation Metrics
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. In this paper,…
ExSum: From Local Explanations to Model Understanding
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. Fulfilling this goal requires bot…
The Irrationality of Neural Rationale Models
Yiming Zheng, Serena Booth, Julie Shah +1
Neural rationale models are popular for interpretable predictions of NLP tasks. In these, a selector extracts segments of the input text, called rationales, and passes these segmen…
Do Feature Attribution Methods Correctly Attribute Features?
Yilun Zhou, Serena Booth, Marco Tulio Ribeiro +1
Feature attribution methods are popular in interpretable machine learning. These methods compute the attribution of each input feature to represent its importance, but there is no…
Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li +3
Active learning (AL) algorithms may achieve better performance with fewer data because the model guides the data selection process. While many algorithms have been proposed, there…
Long-Term Resource Allocation Fairness in Average Markov Decision Process (AMDP) Environment
Ganesh Ghalme, Vineet Nair, Vishakha Patil +1
Fairness has emerged as an important concern in automated decision-making in recent years, especially when these decisions affect human welfare. In this work, we study fairness in…
RoCUS: Robot Controller Understanding via Sampling
Yilun Zhou, Serena Booth, Nadia Figueroa +1
As robots are deployed in complex situations, engineers and end users must develop a holistic understanding of their behaviors, capabilities, and limitations. Some behaviors are di…
Bayes-TrEx: a Bayesian Sampling Approach to Model Transparency by Example
Serena Booth, Yilun Zhou, Ankit Shah +1
Post-hoc explanation methods are gaining popularity for interpreting, understanding, and debugging neural networks. Most analyses using such methods explain decisions in response t…
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach
Serena Booth, Ankit Shah, Yilun Zhou +1
Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to subst…
Adversarially Guided Self-Play for Adopting Social Conventions
Mycal Tucker, Yilun Zhou, Julie Shah
Robotic agents must adopt existing social conventions in order to be effective teammates. These social conventions, such as driving on the right or left side of the road, are arbit…
Learning Household Task Knowledge from WikiHow Descriptions
Yilun Zhou, Julie A. Shah, Steven Schockaert
Commonsense procedural knowledge is important for AI agents and robots that operate in a human environment. While previous attempts at constructing procedural knowledge are mostly…
On Memory Mechanism in Multi-Agent Reinforcement Learning
Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich +1
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environ…
Predicting ConceptNet Path Quality Using Crowdsourced Assessments of Naturalness
Yilun Zhou, Steven Schockaert, Julie A. Shah
In many applications, it is important to characterize the way in which two concepts are semantically related. Knowledge graphs such as ConceptNet provide a rich source of informati…
Asymptotically Optimal Planning by Feasible Kinodynamic Planning in State-Cost Space
Kris Hauser, Yilun Zhou
This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of…