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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 - [i50]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) - [i49]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) - [i48]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) - [i47]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) - [i46]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) - [i45]Yujia Wang, Shiqiang Wang, Songtao Lu, Jinghui Chen:
FADAS: Towards Federated Adaptive Asynchronous Optimization. CoRR abs/2407.18365 (2024) - [i44]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) - [i43]Shuchen Zhu, Boao Kong, Songtao Lu, Xinmeng Huang, Kun Yuan:
SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization. CoRR abs/2411.14166 (2024) - [i42]Xiaodong Cui, A. F. M. Saif, Songtao Lu, Lisha Chen, Tianyi Chen, Brian Kingsbury, George Saon:
Bilevel Joint Unsupervised and Supervised Training for Automatic Speech Recognition. CoRR abs/2412.08548 (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]