6 citations · 11 across the 5 of their papers we have counts for
6 papers
Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models
Preetam Prabhu Srikar Dammu, Yunhe Feng, Chirag Shah
Machine learning (ML) technologies are known to be riddled with ethical and operational problems, however, we are witnessing an increasing thrust by businesses to deploy them in se…
RecRec: Algorithmic Recourse for Recommender Systems
Sahil Verma, Ashudeep Singh, Varich Boonsanong +2
Recommender systems play an essential role in the choices people make in domains such as entertainment, shopping, food, news, employment, and education. The machine learning models…
RecXplainer: Amortized Attribute-based Personalized Explanations for Recommender Systems
Sahil Verma, Chirag Shah, John P. Dickerson +3
Recommender systems influence many of our interactions in the digital world -- impacting how we shop for clothes, sorting what we see when browsing YouTube or TikTok, and determini…
EGCR: Explanation Generation for Conversational Recommendation
Bingbing Wen, Xiaoning Bu, Chirag Shah
Growing attention has been paid in Conversational Recommendation System (CRS), which works as a conversation-based and recommendation task-oriented tool to provide items of interes…
Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems
Bingbing Wen, Yunhe Feng, Yongfeng Zhang +1
As recommender systems become increasingly sophisticated and complex, they often suffer from lack of fairness and transparency. Providing robust and unbiased explanations for recom…
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Sahil Verma, Varich Boonsanong, Minh Hoang +3
Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. Explaining, in a human-understa…