8 citations · 19 across the 10 of their papers we have counts for
4 papers · 1 filter
Evaluating Aleatoric Uncertainty via Conditional Generative Models
Ziyi Huang, Henry Lam, Haofeng Zhang
Aleatoric uncertainty quantification seeks for distributional knowledge of random responses, which is important for reliability analysis and robustness improvement in machine learn…
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Framework
Ziyi Huang, Henry Lam, Amirhossein Meisami +1
Bayesian bandit algorithms with approximate Bayesian inference have been widely used in real-world applications. However, there is a large discrepancy between the superior practica…
Quantifying Epistemic Uncertainty in Deep Learning
Ziyi Huang, Henry Lam, Haofeng Zhang
Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, espec…
Co-Seg: An Image Segmentation Framework Against Label Corruption
Ziyi Huang, Haofeng Zhang, Andrew Laine +3
Supervised deep learning performance is heavily tied to the availability of high-quality labels for training. Neural networks can gradually overfit corrupted labels if directly tra…