most citedRademacher upper bounds for cross-validation errors with an application to the lasso

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

cs.LG2020

Compact Learning for Multi-Label Classification

Jiaqi Lv, Tianran Wu, Chenglun Peng +3

Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC enc…

stat.ML2020★ 2 cited

Rademacher upper bounds for cross-validation errors with an application to the lasso

Ning Xu, Timothy C. G. Fisher, Jian Hong

We establish a general upper bound for -fold cross-validation (-CV) errors that can be adapted to many -CV-based estimators and learning algorithms. Based on Rademacher co…

eess.AS2019

Singing voice conversion with non-parallel data

Xin Chen, Wei Chu, Jinxi Guo +1

Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voic…