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ORCIDKe Bai 0001
Person information
- affiliation (PhD 2023): Duke University, Electrical and Computer Engineering, Durham, NC, USA
Other persons with the same name
- Ke Bai — disambiguation page
- Ke Bai 0002 — Arizona State University, Compiler Microarchitecture Lab, Tempe, USA
- Ke Bai 0003 — Zhengzhou University, School of Electrical Engineering, China
- Ke Bai 0004
![[0000-0002-3523-9009]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-3523-9009")
— Henan University, School of Artificial Intelligence, School of Computer and Information Engineering, Zhengzhou, China - Ke Bai 0005 — Beijing Institute of Petrochemical Technology, School of Safety Engineering, China
- Ke Bai 0006 — Shandong Jianzhu University, School of Computer Science and Technology, Jinan, China
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2020 – today
- 2023
[b1]Ke Bai:
Learning in the Open World: Techniques for Identifying and Adapting to the Unknown. Duke University, Durham, NC, USA, 2023- [j1]Hao Zhang, Long Tian, Zhengjue Wang, Yishi Xu, Pengyu Cheng, Ke Bai, Bo Chen:
Multiscale Visual-Attribute Co-Attention for Zero-Shot Image Recognition. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6003-6014 (2023) - [c11]Ke Bai, Pengyu Cheng, Weituo Hao, Ricardo Henao, Larry Carin:
Estimating Total Correlation with Mutual Information Estimators. AISTATS 2023: 2147-2164 - [c10]Zhan Shi, Guoyin Wang, Ke Bai, Jiwei Li, Xiang Li, Qingjun Cui, Belinda Zeng, Trishul Chilimbi, Xiaodan Zhu:
OssCSE: Overcoming Surface Structure Bias in Contrastive Learning for Unsupervised Sentence Embedding. EMNLP 2023: 7242-7254 - [i8]Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin:
Open World Classification with Adaptive Negative Samples. CoRR abs/2303.05581 (2023) - [i7]Pengyu Cheng, Jiawen Xie, Ke Bai, Yong Dai, Nan Du:
Everyone Deserves A Reward: Learning Customized Human Preferences. CoRR abs/2309.03126 (2023) - 2022
- [c9]Ke Bai, Guoyin Wang, Jiwei Li, Sunghyun Park, Sungjin Lee, Puyang Xu, Ricardo Henao, Lawrence Carin:
Open World Classification with Adaptive Negative Samples. EMNLP 2022: 4378-4392 - [c8]Siyang Yuan, Yitong Li, Dong Wang, Ke Bai, Lawrence Carin, David E. Carlson:
Learning to Weight Filter Groups for Robust Classification. WACV 2022: 3321-3330 - [i6]Ke Bai, Aonan Zhang, Zhizhong Li, Ricardo Henao, Chong Wang, Lawrence Carin:
Collaborative Anomaly Detection. CoRR abs/2209.09923 (2022) - 2021
- [i5]Junya Chen, Danni Lu, Zidi Xiu, Ke Bai, Lawrence Carin, Chenyang Tao:
Variational Inference with Holder Bounds. CoRR abs/2111.02947 (2021) - 2020
- [c7]Liqun Chen, Ke Bai, Chenyang Tao, Yizhe Zhang, Guoyin Wang, Wenlin Wang, Ricardo Henao, Lawrence Carin:
Sequence Generation with Optimal-Transport-Enhanced Reinforcement Learning. AAAI 2020: 7512-7520 - [c6]Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin:
Advancing weakly supervised cross-domain alignment with optimal transport. BMVC 2020 - [c5]Ruiyi Zhang, Changyou Chen, Xinyuan Zhang, Ke Bai, Lawrence Carin:
Semantic Matching via Optimal Partial Transport. EMNLP (Findings) 2020: 212-222 - [i4]Xingxing Zou, Zhizhong Li, Ke Bai, Dahua Lin, Waikeung Wong:
Regularizing Reasons for Outfit Evaluation with Gradient Penalty. CoRR abs/2002.00460 (2020) - [i3]Siyang Yuan, Ke Bai, Liqun Chen, Yizhe Zhang, Chenyang Tao, Chunyuan Li, Guoyin Wang, Ricardo Henao, Lawrence Carin:
Weakly supervised cross-domain alignment with optimal transport. CoRR abs/2008.06597 (2020)
2010 – 2019
- 2019
- [c4]Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin:
Adversarial Learning of a Sampler Based on an Unnormalized Distribution. AISTATS 2019: 3302-3311 - [c3]Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin:
GO Gradient for Expectation-Based Objectives. ICLR (Poster) 2019 - [c2]Chenyang Tao, Shuyang Dai, Liqun Chen, Ke Bai, Junya Chen, Chang Liu, Ruiyi Zhang, Georgiy V. Bobashev, Lawrence Carin:
Variational Annealing of GANs: A Langevin Perspective. ICML 2019: 6176-6185 - [c1]Chenyang Tao, Liqun Chen, Shuyang Dai, Junya Chen, Ke Bai, Dong Wang, Jianfeng Feng, Wenlian Lu, Georgiy V. Bobashev, Lawrence Carin:
On Fenchel Mini-Max Learning. NeurIPS 2019: 10427-10439 - [i2]Chunyuan Li, Ke Bai, Jianqiao Li, Guoyin Wang, Changyou Chen, Lawrence Carin:
Adversarial Learning of a Sampler Based on an Unnormalized Distribution. CoRR abs/1901.00612 (2019) - [i1]Yulai Cong, Miaoyun Zhao, Ke Bai, Lawrence Carin:
GO Gradient for Expectation-Based Objectives. CoRR abs/1901.06020 (2019)
Coauthor Index
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