54 citations · 82 across the 6 of their papers we have counts for
6 papers
Learning to Split for Automatic Bias Detection
Yujia Bao, Regina Barzilay
Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label…
Learning Stable Classifiers by Transferring Unstable Features
Yujia Bao, Shiyu Chang, Regina Barzilay
While unbiased machine learning models are essential for many applications, bias is a human-defined concept that can vary across tasks. Given only input-label pairs, algorithms may…
Predict then Interpolate: A Simple Algorithm to Learn Stable Classifiers
Yujia Bao, Shiyu Chang, Regina Barzilay
We propose Predict then Interpolate (PI), a simple algorithm for learning correlations that are stable across environments. The algorithm follows from the intuition that when using…
Few-shot Text Classification with Distributional Signatures
Yujia Bao, Menghua Wu, Shiyu Chang +1
In this paper, we explore meta-learning for few-shot text classification. Meta-learning has shown strong performance in computer vision, where low-level patterns are transferable a…
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
Yujia Bao, Zhengyi Deng, Yan Wang +9
PURPOSE: The medical literature relevant to germline genetics is growing exponentially. Clinicians need tools monitoring and prioritizing the literature to understand the clinical…
Deriving Machine Attention from Human Rationales
Yujia Bao, Shiyu Chang, Mo Yu +1
Attention-based models are successful when trained on large amounts of data. In this paper, we demonstrate that even in the low-resource scenario, attention can be learned effectiv…