223 citations · 296 across the 12 of their papers we have counts for
8 papers · 1 filter
Predictive Inference with Feature Conformal Prediction
Jiaye Teng, Chuan Wen, Dinghuai Zhang +3
Conformal prediction is a distribution-free technique for establishing valid prediction intervals. Although conventionally people conduct conformal prediction in the output space,…
Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression
Jing Xu, Jiaye Teng, Yang Yuan +1
One of the major open problems in machine learning is to characterize generalization in the overparameterized regime, where most traditional generalization bounds become inconsiste…
Towards Understanding Generalization via Decomposing Excess Risk Dynamics
Jiaye Teng, Jianhao Ma, Yang Yuan
Generalization is one of the fundamental issues in machine learning. However, traditional techniques like uniform convergence may be unable to explain generalization under overpara…
Secure Data Sharing With Flow Model
Chenwei Wu, Chenzhuang Du, Yang Yuan
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data. We consider a variant of this problem, where t…
Inject Machine Learning into Significance Test for Misspecified Linear Models
Jiaye Teng, Yang Yuan
Due to its strong interpretability, linear regression is widely used in social science, from which significance test provides the significance level of models or coefficients in th…
Adversarial Data Encryption
Yingdong Hu, Liang Zhang, Wei Shan +4
In the big data era, many organizations face the dilemma of data sharing. Regular data sharing is often necessary for human-centered discussion and communication, especially in med…
Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers
Guang-He Lee, Yang Yuan, Shiyu Chang +1
Strong theoretical guarantees of robustness can be given for ensembles of classifiers generated by input randomization. Specifically, an bounded adversary cannot alter the…
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan +4
Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address…