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Yu Yang 0007
Person information
- affiliation: University of California, Los Angeles (UCLA), CA, USA
Other persons with the same name
- Yu Yang — disambiguation page
- Yu Yang 0001
— City University of Hong Kong, School of Data Science, Hong Kong (and 2 more)
- Yu Yang 0002
- Yu Yang 0003
- Yu Yang 0004
- Yu Yang 0005
- Yu Yang 0006
- Yu Yang 0008
- Yu Yang 0009
- Yu Yang 0010
- Yu Yang 0011
- Yu Yang 0012
- Yu Yang 0013 — University of Utah, USA
- Yu Yang 0014
- Yu Yang 0015
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2020 – today
- 2024
- [c11]Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman:
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. AISTATS 2024: 2953-2961 - [c10]Xuxi Chen, Yu Yang, Zhangyang Wang, Baharan Mirzasoleiman:
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality. ICLR 2024 - [c9]Yihao Xue, Ali Payani, Yu Yang, Baharan Mirzasoleiman:
Few-shot Adaptation to Distribution Shifts By Mixing Source and Target Embeddings. ICML 2024 - [i18]Yu Yang, Siddhartha Mishra, Jeffrey N. Chiang
SmallToLarge (S2L): Scalable Data Selection for Fine-tuning Large Language Models by Summarizing Training Trajectories of Small Models. CoRR abs/2403.07384 (2024) - [i17]Dang Nguyen, Wenhan Yang, Rathul Anand, Yu Yang, Baharan Mirzasoleiman:
Memory-efficient Training of LLMs with Larger Mini-batches. CoRR abs/2407.19580 (2024) - 2023
- [c8]Neha Prakriya
NeSSA: Near-Storage Data Selection for Accelerated Machine Learning Training. HotStorage 2023: 8-15 - [c7]Yu Yang, Hao Kang, Baharan Mirzasoleiman:
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. ICML 2023: 39314-39330 - [c6]Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman:
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning. ICML 2023: 39365-39379 - [c5]Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu:
Robust Learning with Progressive Data Expansion Against Spurious Correlation. NeurIPS 2023 - [c4]Quanshi Zhang, Xu Cheng, Xin Wang, Yu Yang, Yingnian Wu:
Network Transplanting for the Functionally Modular Architecture. PRCV (3) 2023: 69-83 - [i16]Yu Yang, Besmira Nushi, Hamid Palangi, Baharan Mirzasoleiman:
Mitigating Spurious Correlations in Multi-modal Models during Fine-tuning. CoRR abs/2304.03916 (2023) - [i15]Yihao Xue, Ali Payani, Yu Yang, Baharan Mirzasoleiman:
Eliminating Spurious Correlations from Pre-trained Models via Data Mixing. CoRR abs/2305.14521 (2023) - [i14]Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman:
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. CoRR abs/2305.18761 (2023) - [i13]Yu Yang, Hao Kang, Baharan Mirzasoleiman:
Towards Sustainable Learning: Coresets for Data-efficient Deep Learning. CoRR abs/2306.01244 (2023) - [i12]Yihe Deng, Yu Yang, Baharan Mirzasoleiman, Quanquan Gu:
Robust Learning with Progressive Data Expansion Against Spurious Correlation. CoRR abs/2306.04949 (2023) - [i11]Siddharth Joshi, Yu Yang, Yihao Xue, Wenhan Yang, Baharan Mirzasoleiman:
Towards Mitigating Spurious Correlations in the Wild: A Benchmark & a more Realistic Dataset. CoRR abs/2306.11957 (2023) - [i10]Xuxi Chen, Yu Yang, Zhangyang Wang, Baharan Mirzasoleiman:
Data Distillation Can Be Like Vodka: Distilling More Times For Better Quality. CoRR abs/2310.06982 (2023) - 2022
- [c3]Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman:
Not All Poisons are Created Equal: Robust Training against Data Poisoning. ICML 2022: 25154-25165 - [c2]Tian Yu Liu, Yu Yang, Baharan Mirzasoleiman:
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack. NeurIPS 2022 - [i9]Tian Yu Liu, Yu Yang, Baharan Mirzasoleiman:
Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attacks. CoRR abs/2208.10224 (2022) - [i8]Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman:
Not All Poisons are Created Equal: Robust Training against Data Poisoning. CoRR abs/2210.09671 (2022)
2010 – 2019
- 2019
- [j1]Quanshi Zhang, Xuan Song, Yu Yang, Haotian Ma, Ryosuke Shibasaki:
Visual graph mining for graph matching. Comput. Vis. Image Underst. 178: 16-29 (2019) - [c1]Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu:
Interpreting CNNs via Decision Trees. CVPR 2019: 6261-6270 - [i7]Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu, Song-Chun Zhu, Quanshi Zhang:
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks. CoRR abs/1901.02184 (2019) - [i6]Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu:
Network Transplanting (extended abstract). CoRR abs/1901.06978 (2019) - [i5]Quanshi Zhang, Yu Yang, Ying Nian Wu:
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract). CoRR abs/1901.07538 (2019) - [i4]Lior Deutsch, Erik Nijkamp, Yu Yang:
A Generative Model for Sampling High-Performance and Diverse Weights for Neural Networks. CoRR abs/1905.02898 (2019) - 2018
- [i3]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Interpreting CNNs via Decision Trees. CoRR abs/1802.00121 (2018) - [i2]Quanshi Zhang, Yu Yang, Ying Nian Wu, Song-Chun Zhu:
Network Transplanting. CoRR abs/1804.10272 (2018) - [i1]Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, Song-Chun Zhu:
Unsupervised Learning of Neural Networks to Explain Neural Networks. CoRR abs/1805.07468 (2018)
Coauthor Index
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