Learning with Symmetric Label Noise: The Importance of Being Unhinged

28 May 2015

Papers citing "Learning with Symmetric Label Noise: The Importance of Being Unhinged"

10 / 10 papers shown

Title
Hide and Seek in Noise Labels: Noise-Robust Collaborative Active Learning with LLM-Powered Assistance Bo Yuan Yulin Chen Yin Zhang Wei Jiang NoLa 101 7 0 03 Apr 2025
Early Stopping Against Label Noise Without Validation Data Suqin Yuan Lei Feng Tongliang Liu NoLa 169 18 0 11 Feb 2025
Enhancing Sample Utilization in Noise-Robust Deep Metric Learning With Subgroup-Based Positive-Pair Selection Zhipeng Yu Qianqian Xu Yangbangyan Jiang Yingfei Sun Qingming Huang NoLa 98 0 0 19 Jan 2025
Geometric-Averaged Preference Optimization for Soft Preference Labels Hiroki Furuta Kuang-Huei Lee Shixiang Shane Gu Y. Matsuo Aleksandra Faust Heiga Zen Izzeddin Gur 76 9 0 31 Dec 2024
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 334 509 0 05 Mar 2020
Making Risk Minimization Tolerant to Label Noise Aritra Ghosh Naresh Manwani P. Sastry NoLa 89 214 0 14 Mar 2014
Super-Samples from Kernel Herding Yutian Chen Max Welling Alex Smola 124 335 0 15 Mar 2012
Noise Tolerance under Risk Minimization Naresh Manwani S. M. I. P. S. Sastry NoLa 122 273 0 24 Sep 2011
Composite Binary Losses Mark D. Reid Robert C. Williamson 110 223 0 17 Dec 2009
Information, Divergence and Risk for Binary Experiments Mark D. Reid Robert C. Williamson 85 231 0 05 Jan 2009