Songtao Lu

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 123

Top Venues

Publications

Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, Neil Zhenqiang Gong
Byzantine-Robust Decentralized Federated Learning. CoRR (2024)
Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang, Kun Yuan
Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic Update and Transient Iteration Complexity. CoRR (2024)
Ying Peng, Yihong Dong, Muqiao Yang, Songtao Lu, Qingjiang Shi
Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition. ICASSP (2024)
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) (2024)
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 (2024)
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 (2024)
Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen
FADAS: Towards Federated Adaptive Asynchronous Optimization. CoRR (2024)
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)
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 (2024)
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)
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)
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)
Quan Xiao, Songtao Lu, Tianyi Chen
An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition. NeurIPS (2023)
Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. CoRR (2023)
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 (2023)
Songtao Lu
Bilevel Optimization with Coupled Decision-Dependent Distributions. ICML (2023)
Pengwei Xing, Songtao Lu, Han Yu
FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models. CoRR (2023)
Songtao Lu, Tian Gao
Meta-Dag: Meta Causal Discovery Via Bilevel Optimization. ICASSP (2023)
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) (2023)
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 (2023)
Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. ICML (2023)
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)
Alex Gu, Songtao Lu, Parikshit Ram, Tsui-Wei Weng
Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. ICLR (2023)
Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. MobiHoc (2023)
Qiu Ji, Guilin Qi, Yuxin Ye, Jiaye Li, Site Li, Jianjie Ren, Songtao Lu
Ontology Revision based on Pre-trained Language Models. CoRR (2023)
Han Shen, Songtao Lu, Xiaodong Cui, Tianyi Chen
Distributed Offline Policy Optimization Over Batch Data. AISTATS (2023)
Hui Wan, Hongkang Li, Songtao Lu, Xiaodong Cui, Marina Danilevsky
How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. CoRR (2023)
Quan Xiao, Songtao Lu, Tianyi Chen
A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition. CoRR (2023)
Xiaodong Cui, Ashish Mittal, Songtao Lu, Wei Zhang, George Saon, Brian Kingsbury
Soft Random Sampling: A Theoretical and Empirical Analysis. CoRR (2023)
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)
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)
Songtao Lu
SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization. NeurIPS (2023)
Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective. ICLR (2022)
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)
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)
Yunfei Teng, Anna Choromanska, Murray Campbell, Songtao Lu, Parikshit Ram, Lior Horesh
Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. ECML/PKDD (1) (2022)
Hairi, Jia Liu, Songtao Lu
Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward. ICLR (2022)
Lisha Chen, Songtao Lu, Tianyi Chen
Understanding Benign Overfitting in Nested Meta Learning. CoRR (2022)
Lisha Chen, Songtao Lu, Tianyi Chen
Understanding Benign Overfitting in Gradient-Based Meta Learning. NeurIPS (2022)
Songtao Lu
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. ICML (2022)
Zepeng Zhang, Songtao Lu, Zengfeng Huang, Ziping Zhao
ASGNN: Graph Neural Networks with Adaptive Structure. CoRR (2022)
Songtao Lu
A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. CoRR (2022)
Songtao Lu, Xiaodong Cui, Mark S. Squillante, Brian Kingsbury, Lior Horesh
Decentralized Bilevel Optimization for Personalized Client Learning. ICASSP (2022)
Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. CoRR (2022)
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) (2022)
Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
Zeroth-Order Optimization for Composite Problems with Functional Constraints. AAAI (2022)
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)
Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
Stochastic Inexact Augmented Lagrangian Method for Nonconvex Expectation Constrained Optimization. CoRR (2022)
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 (2022)
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)
Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. CoRR (2022)
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)
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) (2022)
Xiaodong Cui, Songtao Lu, Brian Kingsbury
Federated Acoustic Modeling for Automatic Speech Recognition. ICASSP (2021)
Xiaodong Cui, Songtao Lu, Brian Kingsbury
Federated Acoustic Modeling For Automatic Speech Recognition. CoRR (2021)
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)
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 (2021)
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) (2021)
Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, Lior Horesh
Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning. AAAI (2021)
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 (2021)
Lewis Liu, Songtao Lu, Tuo Zhao, Zhaoran Wang
Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval. IEEE Trans. Signal Process. 69 (2021)
Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu
On the Convergence of Randomized Bregman Coordinate Descent for Non-Lipschitz Composite Problems. ICASSP (2021)
Qi Lyu, Xiao Fu, Weiran Wang, Songtao Lu
Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective. CoRR (2021)
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 (2021)
Yihong Dong, Ying Peng, Muqiao Yang, Songtao Lu, Qingjiang Shi
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition. CoRR (2021)
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 (2021)
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 (2021)
Shuai Ma, Yunqi Zhang, Hang Li, Songtao Lu, Naofal Al-Dhahir, Sha Zhang, Shiyin Li
Robust Beamforming Design for Covert Communications. CoRR (2021)
Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. AISTATS (2021)
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)
Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Lu, Chia-Mu Yu
Adversarial Examples for Unsupervised Machine Learning Models. CoRR (2021)
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)
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) (2020)
Tianxiang Gao, Songtao Lu, Jia Liu, Chris Chu
Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization. CoRR (2020)
Pu Zhao, Sijia Liu, Parikshit Ram, Songtao Lu, Djallel Bouneffouf, Xue Lin
Learned Fine-Tuner for Incongruous Few-Shot Learning. CoRR (2020)
Songtao Lu, Yawen Zhang, Yunlong Wang
Decentralized Federated Learning for Electronic Health Records. CISS (2020)
Meisam Razaviyayn, Tianjian Huang, Songtao Lu, Maher Nouiehed, Maziar Sanjabi, Mingyi Hong
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. CoRR (2020)
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) (2020)
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 (2020)
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 (2020)
Songtao Lu, Chai Wah Wu
Decentralized Stochastic Non-Convex Optimization over Weakly Connected Time-Varying Digraphs. ICASSP (2020)
Haoran Sun, Songtao Lu, Mingyi Hong
Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking. ICML (2020)
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)
Zhenxun Zhuang, Yunlong Wang, Kezi Yu, Songtao Lu
No-Regret Non-Convex Online Meta-Learning. ICASSP (2020)
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)
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)
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 (2020)
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)
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)
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 (2019)
Songtao Lu, Mingyi Hong, Zhengdao Wang
Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point. ICASSP (2019)
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 (2019)
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 (2019)
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)
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)
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 (2019)
Songtao Lu, Zhengdao Wang
Training Optimization and Performance of Single Cell Uplink System With Massive-Antennas Base Station. IEEE Trans. Commun. 67 (2) (2019)
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 (2019)
Songtao Lu, Zhengdao Wang
Spatial Transmitter Density Allocation for Frequency-Selective Wireless Ad Hoc Networks. IEEE Trans. Wirel. Commun. 18 (1) (2019)
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) (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Songtao Lu