Sharon Yixuan Li

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Yiyou SunUniversity of California, BerkeleyVerified email at berkeley.edu
John HopcroftCornell UniversityVerified email at cs.cornell.edu
Yifei MingSalesforce AI ResearchVerified email at salesforce.com
Sean Xuefeng DuPhD student, University of Wisconsin, MadisonVerified email at wisc.edu
Jingkang YangPhD Candidate, MMLab@NTUVerified email at ntu.edu.sg
Ziwei LiuAssistant Professor, Nanyang Technological UniversityVerified email at ntu.edu.sg
Kaiyang ZhouAssistant Professor, Hong Kong Baptist UniversityVerified email at hkbu.edu.hk
Kilian WeinbergerCornell UniversityVerified email at cornell.edu
Gao Huang （黄高）Associate Professor, Tsinghua UniversityVerified email at tsinghua.edu.cn
Geoff PleissAssistant Professor, University of British ColumbiaVerified email at stat.ubc.ca
Xiaojin ZhuProfessor in Computer Science at University of Wisconsin-MadisonVerified email at cs.wisc.edu
Chuan GuoResearch Scientist, FAIR, MetaVerified email at fb.com
Jeff CluneProfessor, Comp. Sci.,U British Columbia; CIFAR AI Chair, Vector; Senior Research Advisor, DeepMindVerified email at ubc.ca
Jason YosinskiWindscape AI; ML CollectiveVerified email at windscape.ai
Robert D NowakKeith and Jane Morgan Nosbusch Professor in ECE, Grace Wahba Professor of Data ScienceVerified email at wisc.edu
Leitian TaoUniversity of Wisconsin–MadisonVerified email at cs.wisc.edu
Christopher RéComputer Science, Stanford UniversityVerified email at cs.stanford.edu
Zhen FangAAII,University of Technology SydneyVerified email at uts.edu.au
Hyeong Kyu ChoiUniversity of Wisconsin-Madison, Korea UniversityVerified email at wisc.edu
Shawn ImUniversity of Wisconsin-MadisonVerified email at wisc.edu

Sharon Yixuan Li

Other namesYixuan Li

University of Wisconsin-Madison

Verified email at cs.wisc.edu - Homepage

machine learning AI safety and reliability


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Stacked Generative Adversarial Networks X Huang, Y Li, O Poursaeed, J Hopcroft, S Belongie IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017	4111*	2017
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks S Liang, Y Li, R Srikant International Conference on Learning Representation (ICLR'18), 2018	2253	2018
Exploring the limits of weakly supervised pretraining D Mahajan, R Girshick, V Ramanathan, K He, M Paluri, Y Li, A Bharambe, ... Proceedings of the European conference on computer vision (ECCV), 181-196, 2018	1617	2018
Energy-based Out-of-distribution Detection W Liu, X Wang, J Owens, Y Li Advances in Neural Information Processing Systems 33, 2020	1233	2020
Snapshot Ensembles: Train 1, Get M for Free G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger International Conference on Learning Representation (ICLR 2017), 2017	1105	2017
Generalized out-of-distribution detection: A survey J Yang, K Zhou, Y Li, Z Liu International Journal of Computer Vision, 1-28, 2024	806	2024
Out-of-Distribution Detection with Deep Nearest Neighbors Y Sun, Y Ming, X Zhu, Y Li In Proceedings of International Conference on Machine Learning (ICML), 2022	404	2022
ReAct: Out-of-distribution Detection With Rectified Activations Y Sun, C Guo, Y Li Advances in Neural Information Processing Systems 34, 144-157, 2021	403	2021
Convergent Learning: Do different neural networks learn the same representations? Y Li, J Yosinski, J Clune, H Lipson, J Hopcroft International Conference on Learning Representation (ICLR), 2016	359	2016
On the Importance of Gradients for Detecting Distributional Shifts in the Wild R Huang, A Geng, Y Li Advances in Neural Information Processing Systems (NeurIPS), 2021	310	2021
VOS: Learning What You Don't Know by Virtual Outlier Synthesis X Du, Z Wang, M Cai, Y Li Proceedings of the International Conference on Learning Representations 1 (4), 8, 2022	273	2022
Mitigating Neural Network Overconfidence with Logit Normalization H Wei, R Xie, H Cheng, L Feng, B An, Y Li In Proceedings of International Conference on Machine Learning (ICML), 2022	239	2022
MOS: Towards Scaling Out-of-distribution Detection for Large Semantic Space R Huang, Y Li Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021	215	2021
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges M Salehi, H Mirzaei, D Hendrycks, Y Li, MH Rohban, M Sabokrou Transactions on Machine Learning Research (TMLR), 2022	205	2022
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection J Yang, P Wang, D Zou, Z Zhou, K Ding, W Peng, H Wang, G Chen, B Li, ... NeurIPS Datasets and Benchmarks Track, 2022	183	2022
Uncovering the small community structure in large networks: A local spectral approach Y Li, K He, D Bindel, JE Hopcroft Proceedings of the 24th international conference on world wide web, 658-668, 2015	155	2015
DICE: Leveraging Sparsification for Out-of-Distribution Detection Y Sun, Y Li In Proceedings of European Conference on Computer Vision (ECCV), 691-708, 2022	150	2022
PiCO: Contrastive Label Disambiguation for Partial Label Learning H Wang, R Xiao, S Li, L Feng, G Niu, G Chen, J Zhao International Conference on Learning Representations (ICLR), 2022	143	2022
Can multi-label classification networks know what they don't know? H Wang, W Liu, A Bocchieri, Y Li Advances in Neural Information Processing Systems (NeurIPS), 2021	133	2021
ATOM: Robustifying Out-of-distribution Detection Using Outlier Mining J Chen, Y Li, X Wu, Y Liang, S Jha Proceedings of European Conference on Machine Learning and Principles and …, 2021	133	2021

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