261 citations · 1.7k across the 168 of their papers we have counts for
236 papers
Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing
Mehedi Hasan, Moloud Abdar, Abbas Khosravi +5
Although neural networks (especially deep neural networks) have achieved \textit{better-than-human} performance in many fields, their real-world deployment is still questionable du…
Fast Rate Information-theoretic Bounds on Generalization Errors
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
The generalization error of a learning algorithm refers to the discrepancy between the loss of a learning algorithm on training data and that on unseen testing data. Various inform…
Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data
Yuan Sun, Winton Nathan-Roberts, Tien Dung Pham +2
In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty as…
Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling
Yuan Sun, Su Nguyen, Dhananjay Thiruvady +3
Constraint programming (CP) is a powerful technique for solving constraint satisfaction and optimization problems. In CP solvers, the variable ordering strategy used to select whic…
On the Generalization for Transfer Learning: An Information-Theoretic Analysis
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different probability distributions. In t…
Fast Rate Generalization Error Bounds: Variations on a Theme
Xuetong Wu, Jonathan H. Manton, Uwe Aickelin +1
A recent line of works, initiated by Russo and Xu, has shown that the generalization error of a learning algorithm can be upper bounded by information measures. In most of the rele…