TY - GEN
T1 - Provably consistent partial-label learning
AU - Feng, Lei
AU - Lv, Jiaqi
AU - Han, Bo
AU - Xu, Miao
AU - Niu, Gang
AU - Geng, Xin
AU - An, Bo
AU - Sugiyama, Masashi
N1 - Funding Information:
This research was supported by the National Research Foundation, Singapore under its AI Singapore Programme (AISG Award No: AISG-RP-2019-0013), National Satellite of Excellence in Trustworthy Software Systems (Award No: NSOE-TSS2019-01), and NTU. Any opinions, findings and conclu- sions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. JL and XG were supported by NSFC (62076063). BH was supported by the RGC Early Career Scheme No. 22200720, NSFC Young Scientists Fund No. 62006202, HKBU Tier-1 Start-up Grant and HKBU CSD Start-up Grant. GN and MS were supported by JST AIP Acceleration Research Grant Number JPMJCR20U3, Japan.
Publisher copyright:
© (2020) by individual authors and Neural Information Processing Systems Foundation Inc. All rights reserved.
PY - 2020/12/6
Y1 - 2020/12/6
N2 - Partial-label learning (PLL) is a multi-class classification problem,
where each training example is associated with a set of candidate
labels. Even though many practical PLL methods have been proposed in the
last two decades, there lacks a theoretical understanding of the
consistency of those methods - none of the PLL methods hitherto
possesses a generation process of candidate label sets, and then it is
still unclear why such a method works on a specific dataset and when it
may fail given a different dataset. In this paper, we propose the first
generation model of candidate label sets, and develop two PLL methods
that are guaranteed to be provably consistent, i.e., one is
risk-consistent and the other is classifier-consistent. Our methods are
advantageous, since they are compatible with any deep network or
stochastic optimizer. Furthermore, thanks to the generation model, we
would be able to answer the two questions above by testing if the
generation model matches given candidate label sets. Experiments on
benchmark and real-world datasets validate the effectiveness of the
proposed generation model and two PLL methods.
AB - Partial-label learning (PLL) is a multi-class classification problem,
where each training example is associated with a set of candidate
labels. Even though many practical PLL methods have been proposed in the
last two decades, there lacks a theoretical understanding of the
consistency of those methods - none of the PLL methods hitherto
possesses a generation process of candidate label sets, and then it is
still unclear why such a method works on a specific dataset and when it
may fail given a different dataset. In this paper, we propose the first
generation model of candidate label sets, and develop two PLL methods
that are guaranteed to be provably consistent, i.e., one is
risk-consistent and the other is classifier-consistent. Our methods are
advantageous, since they are compatible with any deep network or
stochastic optimizer. Furthermore, thanks to the generation model, we
would be able to answer the two questions above by testing if the
generation model matches given candidate label sets. Experiments on
benchmark and real-world datasets validate the effectiveness of the
proposed generation model and two PLL methods.
UR - http://www.scopus.com/inward/record.url?scp=85106987010&partnerID=8YFLogxK
UR - https://www.proceedings.com/59066.html
U2 - 10.48550/arXiv.2007.08929
DO - 10.48550/arXiv.2007.08929
M3 - Conference contribution
AN - SCOPUS:85106987010
SN - 9781713829546
VL - 14
T3 - Advances in Neural Information Processing Systems
SP - 10948
EP - 10960
BT - 34th Conference on Neural Information Processing Systems (NeurIPS 2020)
A2 - Larochelle, H.
A2 - Ranzato, M.
A2 - Hadsell, R.
A2 - Balcan, M.F.
A2 - Lin, H.
PB - Neural Information Processing Systems Foundation
T2 - 34th Conference on Neural Information Processing Systems, NeurIPS 2020
Y2 - 6 December 2020 through 12 December 2020
ER -