21 citations · 41 across the 11 of their papers we have counts for
11 papers
Eigenfunction Extraction for Ordered Representation Learning
Burak Varıcı, Che-Ping Tsai, Ritabrata Ray +2
Recent advances in representation learning reveal that widely used objectives, such as contrastive and non-contrastive, implicitly perform spectral decomposition of a contextual ke…
Contextures: Representations from Contexts
Runtian Zhai, Kai Yang, Che-Ping Tsai +3
Despite the empirical success of foundation models, we do not have a systematic characterization of the representations that these models learn. In this paper, we establish the con…
CLUE: Concept-Level Uncertainty Estimation for Large Language Models
Yu-Hsiang Wang, Andrew Bai, Che-Ping Tsai +1
Large Language Models (LLMs) have demonstrated remarkable proficiency in various natural language generation (NLG) tasks. Previous studies suggest that LLMs' generation process inv…
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…
Representer Point Selection for Explaining Regularized High-dimensional Models
Che-Ping Tsai, Jiong Zhang, Eli Chien +3
We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model i…
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…
Heavy-tailed Streaming Statistical Estimation
Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan +1
We consider the task of heavy-tailed statistical estimation given streaming -dimensional samples. This could also be viewed as stochastic optimization under heavy-tailed distrib…
Order-free Learning Alleviating Exposure Bias in Multi-label Classification
Che-Ping Tsai, Hung-Yi Lee
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural net…
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu +2
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a rel…
Adversarial Learning of Label Dependency: A Novel Framework for Multi-class Classification
Che-Ping Tsai, Hung-Yi Lee
Recent work has shown that exploiting relations between labels improves the performance of multi-label classification. We propose a novel framework based on generative adversarial…
Transcribing Lyrics From Commercial Song Audio: The First Step Towards Singing Content Processing
Che-Ping Tsai, Yi-Lin Tuan, Lin-shan Lee
Spoken content processing (such as retrieval and browsing) is maturing, but the singing content is still almost completely left out. Songs are human voice carrying plenty of semant…