75 citations · 202 across the 18 of their papers we have counts for
6 papers · 1 filter
Regression with Cost-based Rejection
Xin Cheng, Yuzhou Cao, Haobo Wang +3
Learning with rejection is an important framework that can refrain from making predictions to avoid critical mispredictions by balancing between prediction and rejection. Previous…
In Defense of Softmax Parametrization for Calibrated and Consistent Learning to Defer
Yuzhou Cao, Hussein Mozannar, Lei Feng +2
Enabling machine learning classifiers to defer their decision to a downstream expert when the expert is more accurate will ensure improved safety and performance. This objective ca…
A Generalized Unbiased Risk Estimator for Learning with Augmented Classes
Senlin Shu, Shuo He, Haobo Wang +3
In contrast to the standard learning paradigm where all classes can be observed in training data, learning with augmented classes (LAC) tackles the problem where augmented classes…
DOS: Diverse Outlier Sampling for Out-of-Distribution Detection
Wenyu Jiang, Hao Cheng, Mingcai Chen +2
Modern neural networks are known to give overconfident prediction for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outl…
On the Importance of Feature Separability in Predicting Out-Of-Distribution Error
Renchunzi Xie, Hongxin Wei, Lei Feng +2
Estimating the generalization performance is practically challenging on out-of-distribution (OOD) data without ground-truth labels. While previous methods emphasize the connection…
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
Shiyu Tian, Hongxin Wei, Yiqun Wang +1
Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth l…