52 citations · 132 across the 8 of their papers we have counts for
11 papers
Human-Centered Concept Explanations for Neural Networks
Chih-Kuan Yeh, Been Kim, Pradeep Ravikumar
Understanding complex machine learning models such as deep neural networks with explanations is crucial in various applications. Many explanations stem from the model perspective,…
Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations
Chih-Kuan Yeh, Kuan-Yun Lee, Frederick Liu +1
A popular explainable AI (XAI) approach to quantify feature importance of a given model is via Shapley values. These Shapley values arose in cooperative games, and hence a critical…
Evaluations and Methods for Explanation through Robustness Analysis
Cheng-Yu Hsieh, Chih-Kuan Yeh, Xuanqing Liu +4
Feature based explanations, that provide importance of each feature towards the model prediction, is arguably one of the most intuitive ways to explain a model. In this paper, we e…
Minimizing FLOPs to Learn Efficient Sparse Representations
Biswajit Paria, Chih-Kuan Yeh, Ian E. H. Yen +3
Deep representation learning has become one of the most widely adopted approaches for visual search, recommendation, and identification. Retrieval of such representations from a la…
On the (In)fidelity and Sensitivity for Explanations
Chih-Kuan Yeh, Cheng-Yu Hsieh, Arun Sai Suggala +2
We consider objective evaluation measures of saliency explanations for complex black-box machine learning models. We propose simple robust variants of two notions that have been co…
Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching
Chih-Kuan Yeh, Jianshu Chen, Chengzhu Yu +1
We consider the problem of training speech recognition systems without using any labeled data, under the assumption that the learner can only access to the input utterances and a p…