Search Results for author: Yao-Yuan Yang

Found 10 papers, 10 papers with code

What You See is What You Get: Principled Deep Learning via Distributional Generalization

1 code implementation7 Apr 2022 Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran

In contrast, we show that Differentially-Private (DP) training provably ensures the high-level WYSIWYG property, which we quantify using a notion of distributional generalization.

Understanding Rare Spurious Correlations in Neural Networks

1 code implementation10 Feb 2022 Yao-Yuan Yang, Chi-Ning Chou, Kamalika Chaudhuri

Neural networks are known to use spurious correlations such as background information for classification.

Connecting Interpretability and Robustness in Decision Trees through Separation

1 code implementation14 Feb 2021 Michal Moshkovitz, Yao-Yuan Yang, Kamalika Chaudhuri

We then show that a tighter bound on the size is possible when the data is linearly separated.

A Closer Look at Accuracy vs. Robustness

1 code implementation NeurIPS 2020 Yao-Yuan Yang, Cyrus Rashtchian, Hongyang Zhang, Ruslan Salakhutdinov, Kamalika Chaudhuri

Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning.

libact: Pool-based Active Learning in Python

5 code implementations1 Oct 2017 Yao-Yuan Yang, Shao-Chuan Lee, Yu-An Chung, Tung-En Wu, Si-An Chen, Hsuan-Tien Lin

libact is a Python package designed to make active learning easier for general users.

Active Learning

Cost-Sensitive Reference Pair Encoding for Multi-Label Learning

1 code implementation29 Nov 2016 Yao-Yuan Yang, Kuan-Hao Huang, Chih-Wei Chang, Hsuan-Tien Lin

Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.

Active Learning Multi-Label Classification +1

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