70 citations · 378 across the 17 of their papers we have counts for
18 papers
Sample based Explanations via Generalized Representers
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
We propose a general class of sample based explanations of machine learning models, which we term generalized representers. To measure the effect of a training sample on a model's…
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh +6
Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller t…
Concept Gradient: Concept-based Interpretation Without Linear Assumption
Andrew Bai, Chih-Kuan Yeh, Pradeep Ravikumar +2
Concept-based interpretations of black-box models are often more intuitive for humans to understand. The most widely adopted approach for concept-based interpretation is Concept Ac…
Faith-Shap: The Faithful Shapley Interaction Index
Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
Shapley values, which were originally designed to assign attributions to individual players in coalition games, have become a commonly used approach in explainable machine learning…
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…
First is Better Than Last for Language Data Influence
Chih-Kuan Yeh, Ankur Taly, Mukund Sundararajan +2
The ability to identify influential training examples enables us to debug training data and explain model behavior. Existing techniques to do so are based on the flow of training d…
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 Completeness-aware Concept-Based Explanations in Deep Neural Networks
Chih-Kuan Yeh, Been Kim, Sercan O. Arik +3
Human explanations of high-level decisions are often expressed in terms of key concepts the decisions are based on. In this paper, we study such concept-based explainability for De…
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…
Representer Point Selection for Explaining Deep Neural Networks
Chih-Kuan Yeh, Joon Sik Kim, Ian E. H. Yen +1
We propose to explain the predictions of a deep neural network, by pointing to the set of what we call representer points in the training set, for a given test point prediction. Sp…
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
Chung-Wei Lee, Wei Fang, Chih-Kuan Yeh +1
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input insta…
Learning Deep Latent Spaces for Multi-Label Classification
Chih-Kuan Yeh, Wei-Chieh Wu, Wei-Jen Ko +1
Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input i…
Order-Free RNN with Visual Attention for Multi-Label Classification
Shang-Fu Chen, Yi-Chen Chen, Chih-Kuan Yeh +1
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model e…
Generative-Discriminative Variational Model for Visual Recognition
Chih-Kuan Yeh, Yao-Hung Hubert Tsai, Yu-Chiang Frank Wang
The paradigm shift from shallow classifiers with hand-crafted features to end-to-end trainable deep learning models has shown significant improvements on supervised learning tasks.…
Automatic Bridge Bidding Using Deep Reinforcement Learning
Chih-Kuan Yeh, Hsuan-Tien Lin
Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which r…