41 citations · 79 across the 7 of their papers we have counts for
16 papers
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Yuxin Xiao, Paul Pu Liang, Umang Bhatt +3
Pre-trained language models (PLMs) have gained increasing popularity due to their compelling prediction performance in diverse natural language processing (NLP) tasks. When formula…
On the Utility of Prediction Sets in Human-AI Teams
Varun Babbar, Umang Bhatt, Adrian Weller
Research on human-AI teams usually provides experts with a single label, which ignores the uncertainty in a model's recommendation. Conformal prediction (CP) is a well established…
DIVINE: Diverse Influential Training Points for Data Visualization and Model Refinement
Umang Bhatt, Isabel Chien, Muhammad Bilal Zafar +1
As the complexity of machine learning (ML) models increases, resulting in a lack of prediction explainability, several methods have been developed to explain a model's behavior in…
Do Concept Bottleneck Models Learn as Intended?
Andrei Margeloiu, Matthew Ashman, Umang Bhatt +3
Concept bottleneck models map from raw inputs to concepts, and then from concepts to targets. Such models aim to incorporate pre-specified, high-level concepts into the learning pr…
A Multistakeholder Approach Towards Evaluating AI Transparency Mechanisms
Ana Lucic, Madhulika Srikumar, Umang Bhatt +4
Given that there are a variety of stakeholders involved in, and affected by, decisions from machine learning (ML) models, it is important to consider that different stakeholders ha…
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Umang Bhatt, Javier Antorán, Yunfeng Zhang +12
Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most…