no code implementations • 31 May 2023 • Che-Ping Tsai, Jiong Zhang, Eli Chien, Hsiang-Fu Yu, Cho-Jui Hsieh, Pradeep Ravikumar
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 in terms of importance weights for each of the training samples.
1 code implementation • 2 Mar 2022 • Che-Ping Tsai, Chih-Kuan Yeh, Pradeep Ravikumar
We show that by additionally requiring the faithful interaction indices to satisfy interaction-extensions of the standard individual Shapley axioms (dummy, symmetry, linearity, and efficiency), we obtain a unique Faithful Shapley Interaction index, which we denote Faith-Shap, as a natural generalization of the Shapley value to interactions.
no code implementations • 25 Aug 2021 • Che-Ping Tsai, Adarsh Prasad, Sivaraman Balakrishnan, Pradeep Ravikumar
We consider the task of heavy-tailed statistical estimation given streaming $p$-dimensional samples.
1 code implementation • 8 Sep 2019 • Che-Ping Tsai, Hung-Yi Lee
In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias.
no code implementations • 8 Apr 2019 • Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu, Hung-Yi Lee, Lin-shan Lee
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 relatively big unlabeled data set for such languages is more achievable.
no code implementations • 12 Nov 2018 • Che-Ping Tsai, Hung-Yi Lee
The discriminator learns to model label dependency by discriminating real and generated label sets.
no code implementations • 15 Apr 2018 • 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.