223 citations · 296 across the 12 of their papers we have counts for
12 papers
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
Chenzhuang Du, Jiaye Teng, Tingle Li +5
We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which…
On the Power of Foundation Models
Yang Yuan
With infinitely many high-quality data points, infinite computational power, an infinitely large foundation model with a perfect training algorithm and guaranteed zero generalizati…
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,…
Anomaly Detection with Test Time Augmentation and Consistency Evaluation
Haowei He, Jiaye Teng, Yang Yuan
Deep neural networks are known to be vulnerable to unseen data: they may wrongly assign high confidence stcores to out-distribuion samples. Recent works try to solve the problem us…
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…
T-SCI: A Two-Stage Conformal Inference Algorithm with Guaranteed Coverage for Cox-MLP
Jiaye Teng, Zeren Tan, Yang Yuan
It is challenging to deal with censored data, where we only have access to the incomplete information of survival time instead of its exact value. Fortunately, under linear predict…
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…