6 citations · 10 across the 9 of their papers we have counts for
8 papers · 1 filter
When More Experts Hurt: Underfitting in Multi-Expert Learning to Defer
Shuqi Liu, Yuzhou Cao, Lei Feng +2
Learning to Defer (L2D) enables a classifier to abstain from predictions and defer to an expert, and has recently been extended to multi-expert settings. In this work, we show that…
Non-Stationary Online Structured Prediction with Surrogate Losses
Shinsaku Sakaue, Han Bao, Yuzhou Cao
Online structured prediction, including online classification as a special case, is the task of sequentially predicting labels from input features. In this setting, the surrogate r…
Establishing Linear Surrogate Regret Bounds for Convex Smooth Losses via Convolutional Fenchel-Young Losses
Yuzhou Cao, Han Bao, Lei Feng +1
Surrogate regret bounds, also known as excess risk bounds, bridge the gap between the convergence rates of surrogate and target losses. The regret transfer is lossless if the surro…
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…
Weakly Supervised Regression with Interval Targets
Xin Cheng, Yuzhou Cao, Ximing Li +2
This paper investigates an interesting weakly supervised regression setting called regression with interval targets (RIT). Although some of the previous methods on relevant regress…
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…
Multi-Class Classification from Single-Class Data with Confidences
Yuzhou Cao, Lei Feng, Senlin Shu +4
Can we learn a multi-class classifier from only data of a single class? We show that without any assumptions on the loss functions, models, and optimizers, we can successfully lear…