default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSongtao Lu
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j28]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
:
Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. Comput. Optim. Appl. 87(1): 117-147 (2024)- [j27]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Correction to: Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. Comput. Optim. Appl. 89(2): 575-578 (2024) - [j26]Pengwei Xing, Songtao Lu, Lingfei Wu, Han Yu:
BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning. IEEE Trans. Big Data 10(6): 903-914 (2024) - [c75]Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, Neil Gong:
Byzantine-Robust Decentralized Federated Learning. CCS 2024: 2874-2888 - [c74]Ying Peng, Yihong Dong, Muqiao Yang, Songtao Lu, Qingjiang Shi:
Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition. ICASSP 2024: 5445-5449 - [c73]Heshan Devaka Fernando, Lisha Chen, Songtao Lu, Pin-Yu Chen, Miao Liu, Subhajit Chaudhury, Keerthiram Murugesan, Gaowen Liu, Meng Wang, Tianyi Chen:
Variance Reduction Can Improve Trade-Off in Multi-Objective Learning. ICASSP 2024: 6975-6979 - [c72]Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky:
How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context. ICASSP 2024: 9991-9995 - [c71]A F. M. Saif, Xiaodong Cui, Han Shen, Songtao Lu, Brian Kingsbury, Tianyi Chen:
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization. ICASSP 2024: 10931-10935 - [c70]Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, Songtao Lu:
PILOT: An $\mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation. ICLR 2024 - [c69]Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang:
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. ICML 2024 - [c68]Yutong He, Jie Hu, Xinmeng Huang, Songtao Lu, Bin Wang, Kun Yuan:
Distributed Bilevel Optimization with Communication Compression. ICML 2024 - [c67]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? ICML 2024 - [c66]Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen:
FADAS: Towards Federated Adaptive Asynchronous Optimization. ICML 2024 - [c65]Pengwei Xing, Songtao Lu, Han Yu:
Federated Neuro-Symbolic Learning. ICML 2024 - [i48]A F. M. Saif, Xiaodong Cui, Han Shen, Songtao Lu, Brian Kingsbury, Tianyi Chen:
Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization. CoRR abs/2401.06980 (2024) - [i47]Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang, Kun Yuan:
Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic Update and Transient Iteration Complexity. CoRR abs/2402.03167 (2024) - [i46]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis. CoRR abs/2402.15607 (2024) - [i45]Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang:
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. CoRR abs/2405.15920 (2024) - [i44]Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, Neil Zhenqiang Gong:
Byzantine-Robust Decentralized Federated Learning. CoRR abs/2406.10416 (2024) - [i43]Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen:
FADAS: Towards Federated Adaptive Asynchronous Optimization. CoRR abs/2407.18365 (2024) - [i42]Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen:
Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. CoRR abs/2410.02167 (2024) - 2023
- [j25]Shuai Ma, Shiyu Cao, Hang Li, Songtao Lu, Tingting Yang, Youlong Wu, Naofal Al-Dhahir, Shiyin Li:
Waveform Design and Optimization for Integrated Visible Light Positioning and Communication. IEEE Trans. Commun. 71(9): 5392-5407 (2023) - [c64]Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen:
Distributed Offline Policy Optimization Over Batch Data. AISTATS 2023: 4443-4472 - [c63]Songtao Lu, Tian Gao:
Meta-Dag: Meta Causal Discovery Via Bilevel Optimization. ICASSP 2023: 1-5 - [c62]Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu:
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. ICLR 2023 - [c61]Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng:
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. ICLR 2023 - [c60]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. ICML 2023: 22420-22453 - [c59]Songtao Lu:
Bilevel Optimization with Coupled Decision-Dependent Distributions. ICML 2023: 22758-22789 - [c58]Yonggui Yan, Jie Chen, Pin-Yu Chen, Xiaodong Cui, Songtao Lu, Yangyang Xu:
Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. ICML 2023: 39035-39061 - [c57]Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu:
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. MobiHoc 2023: 191-200 - [c56]Songtao Lu:
SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization. NeurIPS 2023 - [c55]Quan Xiao, Songtao Lu, Tianyi Chen:
An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition. NeurIPS 2023 - [c54]Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. NeurIPS 2023 - [i41]Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu:
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. CoRR abs/2302.02922 (2023) - [i40]Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu:
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. CoRR abs/2303.02532 (2023) - [i39]Quan Xiao, Songtao Lu, Tianyi Chen:
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition. CoRR abs/2306.02422 (2023) - [i38]Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky:
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. CoRR abs/2308.13760 (2023) - [i37]Pengwei Xing, Songtao Lu, Han Yu:
FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models. CoRR abs/2308.15324 (2023) - [i36]Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury:
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. CoRR abs/2310.16173 (2023) - [i35]Qiu Ji, Guilin Qi, Yuxin Ye, Jiaye Li, Site Li, Jianjie Ren, Songtao Lu:
Ontology Revision based on Pre-trained Language Models. CoRR abs/2310.18378 (2023) - [i34]Xiaodong Cui, Ashish R. Mittal, Songtao Lu, Wei Zhang, George Saon, Brian Kingsbury:
Soft Random Sampling: A Theoretical and Empirical Analysis. CoRR abs/2311.12727 (2023) - 2022
- [j24]Lunchen Xie, Jiaqi Liu, Songtao Lu, Tsung-Hui Chang, Qingjiang Shi:
An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization. ACM Trans. Intell. Syst. Technol. 13(5): 77:1-77:28 (2022) - [j23]Shuai Ma, Fan Zhang, Songtao Lu, Hang Li, Ruixin Yang, Sihua Shao, Jiaheng Wang, Shiyin Li:
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks. IEEE Trans. Wirel. Commun. 21(6): 3979-3993 (2022) - [c53]Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Chia-Mu Yu:
Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning. AAAI 2022: 6926-6934 - [c52]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Zeroth-Order Optimization for Composite Problems with Functional Constraints. AAAI 2022: 7453-7461 - [c51]Songtao Lu, Xiaodong Cui, Mark S. Squillante, Brian Kingsbury, Lior Horesh:
Decentralized Bilevel Optimization for Personalized Client Learning. ICASSP 2022: 5543-5547 - [c50]Hairi, Jia Liu, Songtao Lu:
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward. ICLR 2022 - [c49]Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu:
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective. ICLR 2022 - [c48]Songtao Lu:
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. ICML 2022: 14315-14357 - [c47]Pu Zhao, Parikshit Ram, Songtao Lu, Yuguang Yao, Djallel Bouneffouf, Xue Lin, Sijia Liu:
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. IJCAI 2022: 1714-1720 - [c46]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
INTERACT: achieving low sample and communication complexities in decentralized bilevel learning over networks. MobiHoc 2022: 61-70 - [c45]Lisha Chen, Songtao Lu, Tianyi Chen:
Understanding Benign Overfitting in Gradient-Based Meta Learning. NeurIPS 2022 - [c44]Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark S. Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong:
A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. NeurIPS 2022 - [c43]Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh:
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. ECML/PKDD (1) 2022: 675-692 - [c42]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed adversarial training to robustify deep neural networks at scale. UAI 2022: 2353-2363 - [i33]Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng:
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. CoRR abs/2203.01924 (2022) - [i32]Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu:
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale. CoRR abs/2206.06257 (2022) - [i31]Lisha Chen, Songtao Lu, Tianyi Chen:
Understanding Benign Overfitting in Nested Meta Learning. CoRR abs/2206.13482 (2022) - [i30]Songtao Lu:
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. CoRR abs/2207.05650 (2022) - [i29]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. CoRR abs/2207.13283 (2022) - [i28]Zepeng Zhang, Songtao Lu, Zengfeng Huang, Ziping Zhao:
ASGNN: Graph Neural Networks with Adaptive Structure. CoRR abs/2210.01002 (2022) - [i27]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization. CoRR abs/2212.09513 (2022) - 2021
- [j22]Shuai Ma, Ruixin Yang, Yang He, Songtao Lu, Fuhui Zhou, Naofal Al-Dhahir, Shiyin Li:
Achieving Channel Capacity of Visible Light Communication. IEEE Syst. J. 15(2): 1652-1663 (2021) - [j21]Shuai Ma, Yunqi Zhang, Hang Li, Songtao Lu, Naofal Al-Dhahir, Sha Zhang, Shiyin Li:
Robust Beamforming Design for Covert Communications. IEEE Trans. Inf. Forensics Secur. 16: 3026-3038 (2021) - [j20]Lewis Liu, Songtao Lu, Tuo Zhao, Zhaoran Wang:
Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval. IEEE Trans. Signal Process. 69: 3991-4006 (2021) - [j19]Songtao Lu, Jason D. Lee, Meisam Razaviyayn, Mingyi Hong:
Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. IEEE Trans. Signal Process. 69: 4859-4874 (2021) - [c41]Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, Lior Horesh:
Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning. AAAI 2021: 8767-8775 - [c40]Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu:
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. AISTATS 2021: 2170-2178 - [c39]Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu:
On the Convergence of Randomized Bregman Coordinate Descent for Non-Lipschitz Composite Problems. ICASSP 2021: 5549-5553 - [c38]Songtao Lu, Naweed Khan, Ismail Yunus Akhalwaya, Ryan Riegel, Lior Horesh, Alexander G. Gray:
Training Logical Neural Networks by Primal-Dual Methods for Neuro-Symbolic Reasoning. ICASSP 2021: 5559-5563 - [c37]Xiaodong Cui, Songtao Lu, Brian Kingsbury:
Federated Acoustic Modeling for Automatic Speech Recognition. ICASSP 2021: 6748-6752 - [c36]Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu:
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. NeurIPS 2021: 18825-18838 - [i26]Xiaodong Cui, Songtao Lu, Brian Kingsbury:
Federated Acoustic Modeling For Automatic Speech Recognition. CoRR abs/2102.04429 (2021) - [i25]Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, Chia-Mu Yu:
Adversarial Examples for Unsupervised Machine Learning Models. CoRR abs/2103.01895 (2021) - [i24]Shuai Ma, Yunqi Zhang, Hang Li, Songtao Lu, Naofal Al-Dhahir, Sha Zhang, Shiyin Li:
Robust Beamforming Design for Covert Communications. CoRR abs/2103.16786 (2021) - [i23]Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan:
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. CoRR abs/2104.11125 (2021) - [i22]Lunchen Xie, Jiaqi Liu, Songtao Lu, Tsung-Hui Chang, Qingjiang Shi:
An Efficient Learning Framework For Federated XGBoost Using Secret Sharing And Distributed Optimization. CoRR abs/2105.05717 (2021) - [i21]Yihong Dong, Ying Peng, Muqiao Yang, Songtao Lu, Qingjiang Shi:
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition. CoRR abs/2106.04392 (2021) - [i20]Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu:
Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective. CoRR abs/2106.07115 (2021) - [i19]Shuai Ma, Fan Zhang, Songtao Lu, Hang Li, Ruixin Yang, Sihua Shao, Jiaheng Wang, Shiyin Li:
Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks. CoRR abs/2111.02581 (2021) - 2020
- [j18]Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, Songtao Lu:
Distributed Learning in the Nonconvex World: From batch data to streaming and beyond. IEEE Signal Process. Mag. 37(3): 26-38 (2020) - [j17]Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong:
Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. IEEE Signal Process. Mag. 37(5): 55-66 (2020) - [j16]Songtao Lu, Ioannis C. Tsaknakis, Mingyi Hong, Yongxin Chen:
Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications. IEEE Trans. Signal Process. 68: 3676-3691 (2020) - [j15]Chun Du, Shuai Ma, Yang He, Songtao Lu, Hang Li, Han Zhang, Shiyin Li:
Nonorthogonal Multiple Access for Visible Light Communication IoT Networks. Wirel. Commun. Mob. Comput. 2020: 5791436:1-5791436:10 (2020) - [c35]Songtao Lu, Yawen Zhang, Yunlong Wang:
Decentralized Federated Learning for Electronic Health Records. CISS 2020: 1-5 - [c34]Zhenxun Zhuang, Yunlong Wang, Kezi Yu, Songtao Lu:
No-Regret Non-Convex Online Meta-Learning. ICASSP 2020: 3942-3946 - [c33]Songtao Lu, Chai Wah Wu:
Decentralized Stochastic Non-Convex Optimization over Weakly Connected Time-Varying Digraphs. ICASSP 2020: 5770-5774 - [c32]Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly:
Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks. ICML 2020: 6282-6293 - [c31]Haoran Sun, Songtao Lu, Mingyi Hong:
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking. ICML 2020: 9217-9228 - [c30]Gang Wang, Songtao Lu, Georgios B. Giannakis, Gerald Tesauro, Jian Sun:
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. NeurIPS 2020 - [c29]Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan:
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. NeurIPS 2020 - [c28]Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. NeurIPS 2020 - [i18]Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, Songtao Lu:
Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond. CoRR abs/2001.04786 (2020) - [i17]Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu:
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization. CoRR abs/2001.05202 (2020) - [i16]Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong:
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. CoRR abs/2006.08141 (2020) - [i15]Pu Zhao, Sijia Liu, Parikshit Ram, Songtao Lu, Djallel Bouneffouf, Xue Lin:
Learned Fine-Tuner for Incongruous Few-Shot Learning. CoRR abs/2009.13714 (2020)
2010 – 2019
- 2019
- [j14]Shuai Ma, Jiahui Dai, Songtao Lu, Hang Li, Han Zhang, Chun Du, Shiyin Li:
Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms. IEEE Access 7: 30588-30598 (2019) - [j13]Hongmei Wang, Zhenzhen Wu, Shuai Ma, Songtao Lu, Han Zhang, Guoru Ding, Shiyin Li:
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics. IEEE Access 7: 30792-30801 (2019) - [j12]Songtao Lu, Zhengdao Wang:
Training Optimization and Performance of Single Cell Uplink System With Massive-Antennas Base Station. IEEE Trans. Commun. 67(2): 1570-1585 (2019) - [j11]Shuai Ma, Yang He, Hang Li, Songtao Lu, Fan Zhang, Shiyin Li:
Optimal Power Allocation for Mobile Users in Non-Orthogonal Multiple Access Visible Light Communication Networks. IEEE Trans. Commun. 67(3): 2233-2244 (2019) - [j10]Shuai Ma, Hang Li, Yang He, Ruixin Yang, Songtao Lu, Wen Cao, Shiyin Li:
Capacity Bounds and Interference Management for Interference Channel in Visible Light Communication Networks. IEEE Trans. Wirel. Commun. 18(1): 182-193 (2019) - [j9]Songtao Lu, Zhengdao Wang:
Spatial Transmitter Density Allocation for Frequency-Selective Wireless Ad Hoc Networks. IEEE Trans. Wirel. Commun. 18(1): 473-486 (2019) - [c27]Songtao Lu, Rahul Singh, Xiangyi Chen, Yongxin Chen, Mingyi Hong:
Alternating Gradient Descent Ascent for Nonconvex Min-Max Problems in Robust Learning and GANs. ACSSC 2019: 680-684 - [c26]Songtao Lu, Xinwei Zhang, Haoran Sun, Mingyi Hong:
GNSD: a Gradient-Tracking Based Nonconvex Stochastic Algorithm for Decentralized Optimization. DSW 2019: 315-321 - [c25]Songtao Lu, Mingyi Hong, Zhengdao Wang:
Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point. ICASSP 2019: 2907-2911 - [c24]Songtao Lu, Ioannis C. Tsaknakis, Mingyi Hong:
Block Alternating Optimization for Non-convex Min-max Problems: Algorithms and Applications in Signal Processing and Communications. ICASSP 2019: 4754-4758 - [c23]Songtao Lu, Ziping Zhao, Kejun Huang, Mingyi Hong:
Perturbed Projected Gradient Descent Converges to Approximate Second-order Points for Bound Constrained Nonconvex Problems. ICASSP 2019: 5356-5360 - [c22]Songtao Lu, Mingyi Hong, Zhengdao Wang:
PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization. ICML 2019: 4134-4143 - [i14]Hongmei Wang, Zhenzhen Wu, Shuai Ma, Songtao Lu, Han Zhang, Guoru Ding, Shiyin Li:
Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics. CoRR abs/1903.04297 (2019) - [i13]Shuai Ma, Jiahui Dai, Songtao Lu, Hang Li, Han Zhang, Chun Du, Shiyin Li:
Signal Demodulation with Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset and Algorithms. CoRR abs/1903.11385 (2019) - [i12]Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong:
SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems. CoRR abs/1907.04450 (2019) - [i11]Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly:
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML. CoRR abs/1909.13806 (2019) - [i10]Haoran Sun, Songtao Lu, Mingyi Hong:
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach. CoRR abs/1910.05857 (2019) - [i9]Zhenxun Zhuang, Yunlong Wang, Kezi Yu, Songtao Lu:
Online Meta-Learning on Non-convex Setting. CoRR abs/1910.10196 (2019) - [i8]Songtao Lu, Yawen Zhang, Yunlong Wang, Christina Mack:
Learn Electronic Health Records by Fully Decentralized Federated Learning. CoRR abs/1912.01792 (2019) - [i7]Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu:
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems. CoRR abs/1912.07527 (2019) - 2018
- [j8]Yu Zhao, Shaohua Wan, Songtao Lu, Jinping Sun, Peng Lei:
Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided Space-Time Adaptive Processing. IEEE Access 6: 68001-68012 (2018) - [c21]Ziping Zhao, Songtao Lu, Mingyi Hong, Daniel P. Palomar:
Distributed optimization for Generalized Phase Retrieval Over Networks. ACSSC 2018: 48-52 - [c20]Yongli Zhu, Renchang Dai, Guangyi Liu, Zhiwei Wang, Songtao Lu:
Power Market Price Forecasting via Deep Learning. IECON 2018: 4935-4939 - [i6]Songtao Lu, Mingyi Hong, Zhengdao Wang:
On the Sublinear Convergence of Randomly Perturbed Alternating Gradient Descent to Second Order Stationary Solutions. CoRR abs/1802.10418 (2018) - [i5]Yongli Zhu, Songtao Lu, Renchang Dai, Guangyi Liu, Zhiwei Wang:
Power Market Price Forecasting via Deep Learning. CoRR abs/1809.08092 (2018) - 2017
- [j7]Chaoyun Mai, Songtao Lu, Jinping Sun, Guohua Wang:
Beampattern Optimization for Frequency Diverse Array With Sparse Frequency Waveforms. IEEE Access 5: 17914-17926 (2017) - [j6]Songtao Lu, Mingyi Hong, Zhengdao Wang:
A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality. IEEE Trans. Signal Process. 65(12): 3120-3135 (2017) - [c19]Songtao Lu, Mingyi Hong, Zhengdao Wang:
A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization. AISTATS 2017: 812-821 - [c18]Xuwang Zhang, Jinping Sun, Songtao Lu:
Spectrum Zoom Processing for Low-Altitude and Slow-Speed Small Target Detection. GSKI (1) 2017: 405-413 - [c17]Tianguang Lu, Wei-Jen Lee, Qian Ai, Songtao Lu:
A priority decision making-based bidding strategy for interactive aggregators. IAS 2017: 1-8 - [c16]Songtao Lu, Mingyi Hong, Zhengdao Wang:
A nonconvex splitting method for symmetric nonnegative matrix factorization: Convergence analysis and optimality. ICASSP 2017: 2572-2576 - [c15]Qi Ming, Songtao Lu:
Overview of System Wide Information Management and Security Analysis. ISADS 2017: 191-194 - 2016
- [j5]Jinbin Fu, Jinping Sun, Songtao Lu, Yingjing Zhang:
Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test. Sci. China Inf. Sci. 59(12): 122306:1-122306:11 (2016) - [j4]Xuwang Zhang, Jinping Sun, Yuxi Zhang, Songtao Lu, Chao Liu:
H-PMHT track-before-detect processing with DP-based track initiation and termination. IET Signal Process. 10(9): 1118-1125 (2016) - [c14]Tianxiang Gao, Sigurdur Olofsson, Songtao Lu:
Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering. GlobalSIP 2016: 247-251 - [i4]Qingjiang Shi, Haoran Sun, Songtao Lu, Mingyi Hong, Meisam Razaviyayn:
Inexact Block Coordinate Descent Methods For Symmetric Nonnegative Matrix Factorization. CoRR abs/1607.03092 (2016) - 2015
- [j3]Songtao Lu, Zhengdao Wang, Zhaohui Wang, Shengli Zhou:
Throughput of Underwater Wireless Ad Hoc Networks With Random Access: A Physical Layer Perspective. IEEE Trans. Wirel. Commun. 14(11): 6257-6268 (2015) - [c13]Songtao Lu, Zhengdao Wang:
Accelerated algorithms for Eigen-Value Decomposition with application to spectral clustering. ACSSC 2015: 355-359 - [c12]Songtao Lu, Ming Qi:
The Sender Controlled Security Model for Message Service. ISADS 2015: 187-191 - [c11]Songtao Lu, Zhengdao Wang:
Joint optimization of power allocation and training duration for uplink multiuser MIMO communications. WCNC 2015: 322-327 - [c10]Songtao Lu, Zhengdao Wang:
Throughput maximization over frequency-selective communication networks. WCNC 2015: 2008-2013 - 2014
- [c9]Songtao Lu, Zhengdao Wang:
Achievable rates of uplink multiuser massive MIMO systems with estimated channels. GLOBECOM 2014: 3772-3777 - [i3]Songtao Lu, Zhengdao Wang:
Joint Optimization of Power Allocation and Training Duration for Uplink Multiuser MIMO Communications. CoRR abs/1409.6059 (2014) - [i2]Songtao Lu, Zhengdao Wang:
Achievable Rates and Training Optimization for Uplink Multiuser Massive MIMO Systems. CoRR abs/1410.7675 (2014) - 2013
- [j2]Huan Wang, Jinping Sun, Songtao Lu, Shaoming Wei:
Factor graph aided multiple hypothesis tracking. Sci. China Inf. Sci. 56(10): 1-6 (2013) - [j1]Yafei Tian, Songtao Lu, Chenyang Yang:
Macro-Pico Amplitude-Space Sharing with Optimized Han-Kobayashi Coding. IEEE Trans. Commun. 61(10): 4404-4415 (2013) - [c8]Songtao Lu, Zhengdao Wang, Zhaohui Wang, Shengli Zhou:
Underwater acoustic network with random access: a physical layer perspective. WUWNet 2013: 34:1-34:5 - 2012
- [c7]Songtao Lu, Desheng Liu, Jinping Sun:
A distributed adaptive GSC beamformer over coordinated antenna arrays network for interference mitigation. ACSCC 2012: 237-242 - [c6]Kai Huang, Songtao Lu, Jingbo Guo:
An Optimized Cooperative Transmission Scheme for Interference Mitigation in Heterogeneous Downlink Network. VTC Fall 2012: 1-5 - [i1]Yafei Tian, Songtao Lu, Chenyang Yang:
Amplitude Space Sharing among the Macro-Cell and Small-Cell Users. CoRR abs/1210.2146 (2012) - 2010
- [c5]Tai Liu, Songtao Lu, Meng Zheng:
Beamforming for Per-Antenna Power Constrained Downlink SINR Optimization. VTC Spring 2010: 1-5 - [c4]Songtao Lu, Tai Liu, Meng Zheng:
A Wideband Space Time Statistical Model for Characterization of Satellite Communication Channel in Dense Multipath Environment. VTC Spring 2010: 1-5 - [c3]Songtao Lu, Yun Zhao:
A multipath mitigation algorithm in Global Navigation Satellite Systems arrays using Independent Component Analysis. WCNIS 2010: 28-32
2000 – 2009
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-12 21:59 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
