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Song Mei
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2020 – today
- 2024
- [j3]Fei Leng
, Song Mei
DVsc: An Automated Framework for Efficiently Detecting Viral Infection from Single-cell Transcriptomics Data. Genom. Proteom. Bioinform. 22(1) (2024) - [c32]Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai:
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. ICLR 2024 - [c31]Licong Lin, Yu Bai, Song Mei:
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. ICLR 2024 - [i37]Leo Zhou, Joao Basso, Song Mei:
Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm. CoRR abs/2402.19456 (2024) - [i36]Ruiqi Zhang, Licong Lin, Yu Bai, Song Mei:
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning. CoRR abs/2404.05868 (2024) - [i35]Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang:
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization. CoRR abs/2404.07771 (2024) - [i34]Song Mei:
U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models. CoRR abs/2404.18444 (2024) - [i33]Yuhang Cai, Jingfeng Wu, Song Mei, Michael Lindsey, Peter L. Bartlett:
Large Stepsize Gradient Descent for Non-Homogeneous Two-Layer Networks: Margin Improvement and Fast Optimization. CoRR abs/2406.08654 (2024) - [i32]Chongyu Fan, Jiancheng Liu, Licong Lin, Jinghan Jia, Ruiqi Zhang, Song Mei, Sijia Liu:
Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning. CoRR abs/2410.07163 (2024) - 2023
- [c30]Kai Peng, Tong Lu, Song Mei, Nannan Xue, Yongchao Shen, Menglan Hu:
Fine-grained IoT device identification method based on self-supervised ViT. ISPA/BDCloud/SocialCom/SustainCom 2023: 145-152 - [c29]Fan Chen, Yu Bai, Song Mei:
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. ICLR 2023 - [c28]Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. ICML 2023: 5104-5161 - [c27]Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei:
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. NeurIPS 2023 - [c26]Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei:
What can a Single Attention Layer Learn? A Study Through the Random Features Lens. NeurIPS 2023 - [i31]Fan Chen, Huan Wang, Caiming Xiong, Song Mei, Yu Bai:
Lower Bounds for Learning in Revealing POMDPs. CoRR abs/2302.01333 (2023) - [i30]Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, Song Mei:
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection. CoRR abs/2306.04637 (2023) - [i29]Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei:
What can a Single Attention Layer Learn? A Study Through the Random Features Lens. CoRR abs/2307.11353 (2023) - [i28]Song Mei, Yuchen Wu:
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical Models. CoRR abs/2309.11420 (2023) - [i27]Licong Lin, Yu Bai, Song Mei:
Transformers as Decision Makers: Provable In-Context Reinforcement Learning via Supervised Pretraining. CoRR abs/2310.08566 (2023) - [i26]Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, Yu Bai:
How Do Transformers Learn In-Context Beyond Simple Functions? A Case Study on Learning with Representations. CoRR abs/2310.10616 (2023) - [i25]Michael Celentano, Zhou Fan, Licong Lin, Song Mei:
Mean-field variational inference with the TAP free energy: Geometric and statistical properties in linear models. CoRR abs/2311.08442 (2023) - 2022
- [c25]Wenxuan Zhao, Zhigang Ju, Yaqiang Zheng, Hongxi Shi, Song Mei:
Extraction, Composition Analysis and Blood Lipid Lowering Activity of Rana chensinensis Ovum Oil. BECB 2022: 45-55 - [c24]Wenxuan Zhao, Hongguan Jiao, Zhigang Ju, Yaqiang Zheng, Hongxi Shi, Song Mei:
Research on Craft Optimization of Wheat Straw Pretreatment. BECB 2022: 69-80 - [c23]Wenxuan Zhao, Zhigang Ju, Hongxi Shi, Song Mei, Yaqiang Zheng:
Screening and Efficacy Evaluation of High-Yielding Manganese Peroxidase Strain. BECB 2022: 107-123 - [c22]Wenxuan Zhao, Zhigang Ju, Yaqiang Zheng, Song Mei, Hongxi Shi:
Breeding and Efficiency Evaluation of a High-Yielding Cellobiohydrolase Strain. BECB 2022: 124-137 - [c21]Wenxuan Zhao, Yaqiang Zheng, Zhigang Ju, Song Mei, Hongxi Shi:
High-Yielding Laccase Strain Breeding and Optimization of Fermentation Conditions. BECB 2022: 209-222 - [c20]Joao Basso, David Gamarnik, Song Mei, Leo Zhou
Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models. FOCS 2022: 335-343 - [c19]Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong:
Efficient and Differentiable Conformal Prediction with General Function Classes. ICLR 2022 - [c18]Nikhil Ghosh, Song Mei, Bin Yu:
The Three Stages of Learning Dynamics in High-dimensional Kernel Methods. ICLR 2022 - [c17]Ziang Song, Song Mei, Yu Bai:
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? ICLR 2022 - [c16]Yu Bai, Chi Jin, Song Mei, Tiancheng Yu:
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. ICML 2022: 1337-1382 - [c15]Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu:
Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent. NeurIPS 2022 - [c14]Theodor Misiakiewicz, Song Mei:
Learning with convolution and pooling operations in kernel methods. NeurIPS 2022 - [c13]Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. NeurIPS 2022 - [i24]Yu Bai, Chi Jin, Song Mei, Tiancheng Yu:
Near-Optimal Learning of Extensive-Form Games with Imperfect Information. CoRR abs/2202.01752 (2022) - [i23]Yu Bai, Song Mei, Huan Wang, Yingbo Zhou, Caiming Xiong:
Efficient and Differentiable Conformal Prediction with General Function Classes. CoRR abs/2202.11091 (2022) - [i22]Joao Basso, David Gamarnik, Song Mei, Leo Zhou:
Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models. CoRR abs/2204.10306 (2022) - [i21]Ziang Song, Song Mei, Yu Bai:
Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games. CoRR abs/2205.07223 (2022) - [i20]Yu Bai, Chi Jin, Song Mei, Ziang Song, Tiancheng Yu:
Efficient Φ-Regret Minimization in Extensive-Form Games via Online Mirror Descent. CoRR abs/2205.15294 (2022) - [i19]Fan Chen, Song Mei, Yu Bai:
Unified Algorithms for RL with Decision-Estimation Coefficients: No-Regret, PAC, and Reward-Free Learning. CoRR abs/2209.11745 (2022) - [i18]Fan Chen, Yu Bai, Song Mei:
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms. CoRR abs/2209.14990 (2022) - [i17]Taejoo Ahn, Licong Lin, Song Mei:
Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control. CoRR abs/2211.02778 (2022) - [i16]Song Mei, Cong Zhen:
A Novel Location Free Link Prediction in Multiplex Social Networks. CoRR abs/2212.06449 (2022) - [i15]Song Mei, Zhiqiang Ye:
Plausible deniability for privacy-preserving data synthesis. CoRR abs/2212.06604 (2022) - 2021
- [c12]Song Mei:
Research on the Patriotic Education for College Students in the New Era: Taking the Enhancement of Cultural Confidence as an Example. CIPAE 2021: 1269-1272 - [c11]Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Learning with invariances in random features and kernel models. COLT 2021: 3351-3418 - [c10]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. ICML 2021: 566-576 - [c9]Zitong Yang, Yu Bai, Song Mei:
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. ICML 2021: 11704-11715 - [c8]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Understanding the Under-Coverage Bias in Uncertainty Estimation. NeurIPS 2021: 18307-18319 - [i14]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification. CoRR abs/2102.07856 (2021) - [i13]Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Learning with invariances in random features and kernel models. CoRR abs/2102.13219 (2021) - [i12]Zitong Yang, Yu Bai, Song Mei:
Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models. CoRR abs/2103.04554 (2021) - [i11]Yu Bai, Song Mei, Huan Wang, Caiming Xiong:
Understanding the Under-Coverage Bias in Uncertainty Estimation. CoRR abs/2106.05515 (2021) - [i10]Michael Celentano, Zhou Fan, Song Mei:
Local convexity of the TAP free energy and AMP convergence for Z2-synchronization. CoRR abs/2106.11428 (2021) - [i9]Ziang Song, Song Mei, Yu Bai:
When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? CoRR abs/2110.04184 (2021) - [i8]Nikhil Ghosh, Song Mei, Bin Yu:
The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods. CoRR abs/2111.07167 (2021) - [i7]Theodor Misiakiewicz, Song Mei:
Learning with convolution and pooling operations in kernel methods. CoRR abs/2111.08308 (2021) - 2020
- [c7]Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
When Do Neural Networks Outperform Kernel Methods? NeurIPS 2020 - [i6]Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
When Do Neural Networks Outperform Kernel Methods? CoRR abs/2006.13409 (2020)
2010 – 2019
- 2019
- [c6]Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. COLT 2019: 2388-2464 - [c5]Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Limitations of Lazy Training of Two-layers Neural Network. NeurIPS 2019: 9108-9118 - [i5]Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit. CoRR abs/1902.06015 (2019) - [i4]Yu Bai, John C. Duchi, Song Mei:
Proximal algorithms for constrained composite optimization, with applications to solving low-rank SDPs. CoRR abs/1903.00184 (2019) - [i3]Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Linearized two-layers neural networks in high dimension. CoRR abs/1904.12191 (2019) - [i2]Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, Andrea Montanari:
Limitations of Lazy Training of Two-layers Neural Networks. CoRR abs/1906.08899 (2019) - 2018
- [i1]Song Mei, Andrea Montanari, Phan-Minh Nguyen:
A Mean Field View of the Landscape of Two-Layers Neural Networks. CoRR abs/1804.06561 (2018) - 2017
- [c4]Song Mei, Theodor Misiakiewicz, Andrea Montanari, Roberto Imbuzeiro Oliveira:
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality. COLT 2017: 1476-1515 - 2015
- [j2]Song Mei, Pingwen Zhang:
On a Molecular Based Q-Tensor Model for Liquid Crystals with Density Variations. Multiscale Model. Simul. 13(3): 977-1000 (2015)
2000 – 2009
- 2009
- [c3]Yunhe Zhang, Zhitang Li, Song Mei, Cai Fu:
Session-Based Tunnel Scheduling Model in Multi-link Aggregate IPSec VPN. MUE 2009: 505-510 - 2008
- [c2]Li Xu, Hui Bo, Haixia Liu, Mingqiang Yang, Song Mei, Guo Wei:
Research and Analysis of Topology Control in NS-2 for Ad-hoc Wireless Network. CISIS 2008: 461-465 - [c1]Song Mei:
New Well Design of Hydrological Network Information System Based on GIS. ISCSCT (1) 2008: 698-701 - 2003
- [j1]Zhanjun Chang, Song Mei, Zheng Gu, Jianqin Gu, Liangxiao Xia, Shuang Liang, Jiarui Lin:
Realization of integration and working procedure on digital hospital information system. Comput. Stand. Interfaces 25(5): 529-537 (2003)
Coauthor Index
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last updated on 2024-11-19 21:45 CET by the dblp team
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