2 citations · 5 across the 4 of their papers we have counts for
4 papers
Probabilistically robust conformal prediction
Subhankar Ghosh, Yuanjie Shi, Taha Belkhouja +3
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, C…
Improving Uncertainty Quantification of Deep Classifiers via Neighborhood Conformal Prediction: Novel Algorithm and Theoretical Analysis
Subhankar Ghosh, Taha Belkhouja, Yan Yan +1
Safe deployment of deep neural networks in high-stake real-world applications requires theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled fra…
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives
Zhishuai Guo, Yan Yan, Tianbao Yang
Stochastic gradient descent (SGD) has been widely studied in the literature from different angles, and is commonly employed for solving many big data machine learning problems. How…
Learning Discriminators as Energy Networks in Adversarial Learning
Pingbo Pan, Yan Yan, Tianbao Yang +1
We propose a novel framework for structured prediction via adversarial learning. Existing adversarial learning methods involve two separate networks, i.e., the structured predictio…