Comparison of Phase-Encoded and Sensitivity-Encoded Spectroscopic Imaging. - Min Huang; Songtao Lu; Jiarui Lin; Y. Zhan (MIAR, 2004)

Application of SVD-Based Metabolite Quantification Methods in Magnetic Resonance Spectroscopic Imaging. - Min Huang; Songtao Lu (MIAR, 2006)

Beamforming for Per-Antenna Power Constrained Downlink SINR Optimization. - Tai Liu; Songtao Lu; Meng Zheng (VTC Spring, 2010)

A Wideband Space Time Statistical Model for Characterization of Satellite Communication Channel in Dense Multipath Environment. - Songtao Lu; Tai Liu; Meng Zheng (VTC Spring, 2010)

A multipath mitigation algorithm in Global Navigation Satellite Systems arrays using Independent Component Analysis. - Songtao Lu; Yun Zhao (WCNIS, 2010)

A distributed adaptive GSC beamformer over coordinated antenna arrays network for interference mitigation. - Songtao Lu; Desheng Liu; Jinping Sun (ACSCC, 2012)

An Optimized Cooperative Transmission Scheme for Interference Mitigation in Heterogeneous Downlink Network. - Kai Huang; Songtao Lu; Jingbo Guo (VTC Fall, 2012)

Amplitude Space Sharing among the Macro-Cell and Small-Cell Users - Yafei Tian; Songtao Lu; Chenyang Yang 001 (CoRR, 2012)

Underwater acoustic network with random access: a physical layer perspective. - Songtao Lu; Zhengdao Wang; Zhaohui Wang; Shengli Zhou (WUWNet, 2013)

Factor graph aided multiple hypothesis tracking. - Huan Wang; Jinping Sun; Songtao Lu; Shaoming Wei (Sci. China Inf. Sci., 2013)

Macro-Pico Amplitude-Space Sharing with Optimized Han-Kobayashi Coding. - Yafei Tian; Songtao Lu; Chenyang Yang 001 (IEEE Trans. Commun., 2013)

Achievable rates of uplink multiuser massive MIMO systems with estimated channels. - Songtao Lu; Zhengdao Wang (GLOBECOM, 2014)

Achievable Rates and Training Optimization for Uplink Multiuser Massive MIMO Systems. - Songtao Lu; Zhengdao Wang (CoRR, 2014)

Accelerated algorithms for Eigen-Value Decomposition with application to spectral clustering. - Songtao Lu; Zhengdao Wang (ACSSC, 2015)

The Sender Controlled Security Model for Message Service. - Songtao Lu; Ming Qi (ISADS, 2015)

Joint optimization of power allocation and training duration for uplink multiuser MIMO communications. - Songtao Lu; Zhengdao Wang (WCNC, 2015)

Throughput maximization over frequency-selective communication networks. - Songtao Lu; Zhengdao Wang (WCNC, 2015)

Throughput of Underwater Wireless Ad Hoc Networks With Random Access: A Physical Layer Perspective. - Songtao Lu; Zhengdao Wang; Zhaohui Wang; Shengli Zhou (IEEE Trans. Wirel. Commun., 2015)

Minimum-volume-regularized weighted symmetric nonnegative matrix factorization for clustering. - Tianxiang Gao; Sigurdur Olofsson; Songtao Lu (GlobalSIP, 2016)

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test. - Jinbin Fu; Jinping Sun; Songtao Lu; Yingjing Zhang (Sci. China Inf. Sci., 2016)

Inexact Block Coordinate Descent Methods For Symmetric Nonnegative Matrix Factorization. - Qingjiang Shi; Haoran Sun; Songtao Lu; Mingyi Hong 001; Meisam Razaviyayn (CoRR, 2016)

H-PMHT track-before-detect processing with DP-based track initiation and termination. - Xuwang Zhang; Jinping Sun; Yuxi Zhang; Songtao Lu; Chao Liu 016 (IET Signal Process., 2016)

A Stochastic Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization. - Songtao Lu; Mingyi Hong 001; Zhengdao Wang (AISTATS, 2017)

Spectrum Zoom Processing for Low-Altitude and Slow-Speed Small Target Detection. - Xuwang Zhang; Jinping Sun; Songtao Lu (GSKI (1), 2017)

A priority decision making-based bidding strategy for interactive aggregators. - Tianguang Lu; Wei-Jen Lee; Qian Ai; Songtao Lu (IAS, 2017)

Overview of System Wide Information Management and Security Analysis. - Qi Ming; Songtao Lu (ISADS, 2017)

Beampattern Optimization for Frequency Diverse Array With Sparse Frequency Waveforms. - Chaoyun Mai; Songtao Lu; Jinping Sun; Guohua Wang (IEEE Access, 2017)

A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality. - Songtao Lu; Mingyi Hong 001; Zhengdao Wang (IEEE Trans. Signal Process., 2017)

Distributed optimization for Generalized Phase Retrieval Over Networks. - Ziping Zhao 002; Songtao Lu; Mingyi Hong 001; Daniel P. Palomar (ACSSC, 2018)

Power Market Price Forecasting via Deep Learning. - Yongli Zhu; Renchang Dai; Guangyi Liu 002; Zhiwei Wang 004; Songtao Lu (IECON, 2018)

Exploiting the Persymmetric Property of Covariance Matrices for Knowledge-Aided Space-Time Adaptive Processing. - Yu Zhao 009; Shaohua Wan 001; Songtao Lu; Jinping Sun; Peng Lei (IEEE Access, 2018)

On the Sublinear Convergence of Randomly Perturbed Alternating Gradient Descent to Second Order Stationary Solutions. - Songtao Lu; Mingyi Hong 001; Zhengdao Wang (CoRR, 2018)

Alternating Gradient Descent Ascent for Nonconvex Min-Max Problems in Robust Learning and GANs. - Songtao Lu; Rahul Singh 002; Xiangyi Chen; Yongxin Chen; Mingyi Hong 001 (ACSSC, 2019)

GNSD: a Gradient-Tracking Based Nonconvex Stochastic Algorithm for Decentralized Optimization. - Songtao Lu; Xinwei Zhang 001; Haoran Sun; Mingyi Hong 001 (DSW, 2019)

Fast and Global Optimal Nonconvex Matrix Factorization via Perturbed Alternating Proximal Point. - Songtao Lu; Mingyi Hong 001; Zhengdao Wang (ICASSP, 2019)

Block Alternating Optimization for Non-convex Min-max Problems: Algorithms and Applications in Signal Processing and Communications. - Songtao Lu; Ioannis C. Tsaknakis; Mingyi Hong 001 (ICASSP, 2019)

Perturbed Projected Gradient Descent Converges to Approximate Second-order Points for Bound Constrained Nonconvex Problems. - Songtao Lu; Ziping Zhao 002; Kejun Huang; Mingyi Hong 001 (ICASSP, 2019)

PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization. - Songtao Lu; Mingyi Hong 001; Zhengdao Wang (ICML, 2019)

Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms. - Shuai Ma 002; Jiahui Dai; Songtao Lu; Hang Li 003; Han Zhang 006; Chun Du; Shiyin Li (IEEE Access, 2019)

Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics. - Hongmei Wang; Zhenzhen Wu; Shuai Ma 002; Songtao Lu; Han Zhang 006; Guoru Ding; Shiyin Li (IEEE Access, 2019)

SNAP: Finding Approximate Second-Order Stationary Solutions Efficiently for Non-convex Linearly Constrained Problems. - Songtao Lu; Meisam Razaviyayn; Bo Yang 053; Kejun Huang; Mingyi Hong 001 (CoRR, 2019)

Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML. - Sijia Liu 001; Songtao Lu; Xiangyi Chen; Yao Feng 002; Kaidi Xu; Abdullah Al-Dujaili; Mingyi Hong 001; Una-May O'Reilly (CoRR, 2019)

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: A Joint Gradient Estimation and Tracking Approach. - Haoran Sun; Songtao Lu; Mingyi Hong 001 (CoRR, 2019)

Online Meta-Learning on Non-convex Setting. - Zhenxun Zhuang; Yunlong Wang 010; Kezi Yu; Songtao Lu (CoRR, 2019)

Learn Electronic Health Records by Fully Decentralized Federated Learning. - Songtao Lu; Yawen Zhang; Yunlong Wang 010; Christina Mack (CoRR, 2019)

Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (CoRR, 2019)

Training Optimization and Performance of Single Cell Uplink System With Massive-Antennas Base Station. - Songtao Lu; Zhengdao Wang (IEEE Trans. Commun., 2019)

Optimal Power Allocation for Mobile Users in Non-Orthogonal Multiple Access Visible Light Communication Networks. - Shuai Ma 002; Yang He; Hang Li 003; Songtao Lu; Fan Zhang; Shiyin Li (IEEE Trans. Commun., 2019)

Spatial Transmitter Density Allocation for Frequency-Selective Wireless Ad Hoc Networks. - Songtao Lu; Zhengdao Wang (IEEE Trans. Wirel. Commun., 2019)

Capacity Bounds and Interference Management for Interference Channel in Visible Light Communication Networks. - Shuai Ma 002; Hang Li 003; Yang He; Ruixin Yang; Songtao Lu; Wen Cao; Shiyin Li (IEEE Trans. Wirel. Commun., 2019)

Decentralized Federated Learning for Electronic Health Records. - Songtao Lu; Yawen Zhang; Yunlong Wang 010 (CISS, 2020)

Decentralized Stochastic Non-Convex Optimization over Weakly Connected Time-Varying Digraphs. - Songtao Lu; Chai Wah Wu (ICASSP, 2020)

No-Regret Non-Convex Online Meta-Learning. - Zhenxun Zhuang; Yunlong Wang 010; Kezi Yu; Songtao Lu (ICASSP, 2020)

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks. - Sijia Liu 001; Songtao Lu; Xiangyi Chen; Yao Feng 002; Kaidi Xu; Abdullah Al-Dujaili; Mingyi Hong 001; Una-May O'Reilly (ICML, 2020)

Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking. - Haoran Sun; Songtao Lu; Mingyi Hong 001 (ICML, 2020)

Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. - Gang Wang 014; Songtao Lu; Georgios B. Giannakis; Gerald Tesauro; Jian Sun 003 (NeurIPS, 2020)

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. - Chia-Yu Chen; Jiamin Ni; Songtao Lu; Xiaodong Cui; Pin-Yu Chen; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Wei Zhang 022; Kailash Gopalakrishnan (NeurIPS, 2020)

Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems. - Songtao Lu; Meisam Razaviyayn; Bo Yang 053; Kejun Huang; Mingyi Hong 001 (NeurIPS, 2020)

Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond. - Tsung-Hui Chang; Mingyi Hong 001; Hoi-To Wai; Xinwei Zhang 001; Songtao Lu (CoRR, 2020)

Randomized Bregman Coordinate Descent Methods for Non-Lipschitz Optimization. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (CoRR, 2020)

Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. - Meisam Razaviyayn; Tianjian Huang; Songtao Lu; Maher Nouiehed; Maziar Sanjabi; Mingyi Hong 001 (CoRR, 2020)

Learned Fine-Tuner for Incongruous Few-Shot Learning. - Pu Zhao 001; Sijia Liu 001; Parikshit Ram; Songtao Lu; Djallel Bouneffouf 001; Xue Lin 001 (CoRR, 2020)

Distributed Learning in the Nonconvex World: From batch data to streaming and beyond. - Tsung-Hui Chang; Mingyi Hong 001; Hoi-To Wai; Xinwei Zhang 001; Songtao Lu (IEEE Signal Process. Mag., 2020)

Nonconvex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances. - Meisam Razaviyayn; Tianjian Huang; Songtao Lu; Maher Nouiehed; Maziar Sanjabi; Mingyi Hong 001 (IEEE Signal Process. Mag., 2020)

Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications. - Songtao Lu; Ioannis C. Tsaknakis; Mingyi Hong 001; Yongxin Chen (IEEE Trans. Signal Process., 2020)

Nonorthogonal Multiple Access for Visible Light Communication IoT Networks. - Chun Du; Shuai Ma 002; Yang He; Songtao Lu; Hang Li 003; Han Zhang 006; Shiyin Li (Wirel. Commun. Mob. Comput., 2020)

Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning. - Songtao Lu; Kaiqing Zhang; Tianyi Chen; Tamer Basar; Lior Horesh (AAAI, 2021)

Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization. - Zichong Li; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Yangyang Xu (AISTATS, 2021)

Federated Acoustic Modeling for Automatic Speech Recognition. - Xiaodong Cui; Songtao Lu; Brian Kingsbury (ICASSP, 2021)

On the Convergence of Randomized Bregman Coordinate Descent for Non-Lipschitz Composite Problems. - Tianxiang Gao; Songtao Lu; Jia Liu 002; Chris Chu (ICASSP, 2021)

Training Logical Neural Networks by Primal-Dual Methods for Neuro-Symbolic Reasoning. - Songtao Lu; Naweed Khan; Ismail Yunus Akhalwaya; Ryan Riegel; Lior Horesh; Alexander G. Gray (ICASSP, 2021)

Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. - Xin Zhang 054; Zhuqing Liu; Jia Liu 002; Zhengyuan Zhu; Songtao Lu (NeurIPS, 2021)

Adversarial Examples for Unsupervised Machine Learning Models. - Chia-Yi Hsu; Pin-Yu Chen; Songtao Lu; Sijia Lu; Chia-Mu Yu (CoRR, 2021)

Latent Correlation-Based Multiview Learning and Self-Supervision: A Unifying Perspective. - Qi Lyu; Xiao Fu 001; Weiran Wang; Songtao Lu (CoRR, 2021)

Achieving Channel Capacity of Visible Light Communication. - Shuai Ma 002; Ruixin Yang; Yang He; Songtao Lu; Fuhui Zhou; Naofal Al-Dhahir; Shiyin Li (IEEE Syst. J., 2021)

Robust Beamforming Design for Covert Communications. - Shuai Ma 002; Yunqi Zhang; Hang Li 003; Songtao Lu; Naofal Al-Dhahir; Sha Zhang; Shiyin Li (IEEE Trans. Inf. Forensics Secur., 2021)

Spectrum Truncation Power Iteration for Agnostic Matrix Phase Retrieval. - Lewis Liu; Songtao Lu; Tuo Zhao; Zhaoran Wang 001 (IEEE Trans. Signal Process., 2021)

Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems. - Songtao Lu; Jason D. Lee; Meisam Razaviyayn; Mingyi Hong 001 (IEEE Trans. Signal Process., 2021)

Adversarial Examples Can Be Effective Data Augmentation for Unsupervised Machine Learning. - Chia-Yi Hsu; Pin-Yu Chen; Songtao Lu; Sijia Liu 001; Chia-Mu Yu (AAAI, 2022)

Zeroth-Order Optimization for Composite Problems with Functional Constraints. - Zichong Li; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Yangyang Xu (AAAI, 2022)

Decentralized Bilevel Optimization for Personalized Client Learning. - Songtao Lu; Xiaodong Cui; Mark S. Squillante; Brian Kingsbury; Lior Horesh (ICASSP, 2022)

Finite-Time Convergence and Sample Complexity of Multi-Agent Actor-Critic Reinforcement Learning with Average Reward. - Hairi; Jia Liu 002; Songtao Lu (ICLR, 2022)

Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective. - Qi Lyu; Xiao Fu 001; Weiran Wang; Songtao Lu (ICLR, 2022)

A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization. - Songtao Lu (ICML, 2022)

Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations. - Pu Zhao 001; Parikshit Ram; Songtao Lu; Yuguang Yao; Djallel Bouneffouf 001; Xue Lin 001; Sijia Liu 001 (IJCAI, 2022)

INTERACT: achieving low sample and communication complexities in decentralized bilevel learning over networks. - Zhuqing Liu; Xin Zhang 054; Prashant Khanduri; Songtao Lu; Jia Liu 002 (MobiHoc, 2022)

Understanding Benign Overfitting in Gradient-Based Meta Learning. - Lisha Chen; Songtao Lu; Tianyi Chen (NeurIPS, 2022)

A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization. - Songtao Lu; Siliang Zeng; Xiaodong Cui; Mark S. Squillante; Lior Horesh; Brian Kingsbury; Jia Liu 002; Mingyi Hong 001 (NeurIPS, 2022)

Overcoming Catastrophic Forgetting via Direction-Constrained Optimization. - Yunfei Teng; Anna Choromanska; Murray Campbell; Songtao Lu; Parikshit Ram; Lior Horesh (ECML/PKDD (1), 2022)

Distributed adversarial training to robustify deep neural networks at scale. - Gaoyuan Zhang; Songtao Lu; Yihua Zhang; Xiangyi Chen; Pin-Yu Chen; Quanfu Fan; Lee Martie; Lior Horesh; Mingyi Hong 001; Sijia Liu 001 (UAI, 2022)

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Lily Weng (CoRR, 2022)

Understanding Benign Overfitting in Nested Meta Learning. - Lisha Chen; Songtao Lu; Tianyi Chen (CoRR, 2022)

ASGNN: Graph Neural Networks with Adaptive Structure. - Zepeng Zhang; Songtao Lu; Zengfeng Huang; Ziping Zhao 002 (CoRR, 2022)

An Efficient Learning Framework for Federated XGBoost Using Secret Sharing and Distributed Optimization. - Lunchen Xie; Jiaqi Liu; Songtao Lu; Tsung-Hui Chang; Qingjiang Shi (ACM Trans. Intell. Syst. Technol., 2022)

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks. - Shuai Ma 002; Fan Zhang; Songtao Lu; Hang Li 003; Ruixin Yang; Sihua Shao; Jiaheng Wang 001; Shiyin Li (IEEE Trans. Wirel. Commun., 2022)

Distributed Offline Policy Optimization Over Batch Data. - Han Shen; Songtao Lu; Xiaodong Cui; Tianyi Chen (AISTATS, 2023)

Meta-Dag: Meta Causal Discovery Via Bilevel Optimization. - Songtao Lu; Tian Gao (ICASSP, 2023)

Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks. - Shuai Zhang 015; Meng Wang 003; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Miao Liu 001 (ICLR, 2023)

Min-Max Multi-objective Bilevel Optimization with Applications in Robust Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Tsui-Wei Weng (ICLR, 2023)

Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. - Zhuqing Liu; Xin Zhang 054; Prashant Khanduri; Songtao Lu; Jia Liu 002 (ICML, 2023)

Bilevel Optimization with Coupled Decision-Dependent Distributions. - Songtao Lu (ICML, 2023)

Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data. - Yonggui Yan; Jie Chen; Pin-Yu Chen; Xiaodong Cui; Songtao Lu; Yangyang Xu (ICML, 2023)

PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. - Zhuqing Liu; Xin Zhang 054; Songtao Lu; Jia Liu 002 (MobiHoc, 2023)

SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization. - Songtao Lu (NeurIPS, 2023)

An Alternating Optimization Method for Bilevel Problems under the Polyak-Łojasiewicz Condition. - Quan Xiao; Songtao Lu; Tianyi Chen (NeurIPS, 2023)

On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration. - Shuai Zhang 015; Hongkang Li; Meng Wang 003; Miao Liu 001; Pin-Yu Chen; Songtao Lu; Sijia Liu 001; Keerthiram Murugesan; Subhajit Chaudhury (NeurIPS, 2023)

A Generalized Alternating Method for Bilevel Learning under the Polyak-Łojasiewicz Condition. - Quan Xiao; Songtao Lu; Tianyi Chen (CoRR, 2023)

How Can Context Help? Exploring Joint Retrieval of Passage and Personalized Context. - Hui Wan; Hongkang Li; Songtao Lu; Xiaodong Cui; Marina Danilevsky (CoRR, 2023)

FedLogic: Interpretable Federated Multi-Domain Chain-of-Thought Prompt Selection for Large Language Models. - Pengwei Xing; Songtao Lu; Han Yu 001 (CoRR, 2023)

Ontology Revision based on Pre-trained Language Models. - Qiu Ji; Guilin Qi; Yuxin Ye; Jiaye Li; Site Li; Jianjie Ren; Songtao Lu (CoRR, 2023)

Soft Random Sampling: A Theoretical and Empirical Analysis. - Xiaodong Cui; Ashish R. Mittal; Songtao Lu; Wei Zhang 022; George Saon; Brian Kingsbury (CoRR, 2023)

Waveform Design and Optimization for Integrated Visible Light Positioning and Communication. - Shuai Ma 002; Shiyu Cao; Hang Li 003; Songtao Lu; Tingting Yang; Youlong Wu; Naofal Al-Dhahir; Shiyin Li (IEEE Trans. Commun., 2023)

Byzantine-Robust Decentralized Federated Learning. - Minghong Fang; Zifan Zhang; Hairi; Prashant Khanduri; Jia Liu 002; Songtao Lu; Yuchen Liu 001; Neil Gong 001 (CCS, 2024)

Variance Reduction Can Improve Trade-Off in Multi-Objective Learning. - Heshan Devaka Fernando; Lisha Chen; Songtao Lu; Pin-Yu Chen; Miao Liu 001; Subhajit Chaudhury; Keerthiram Murugesan; Gaowen Liu; Meng Wang 003; Tianyi Chen (ICASSP, 2024)

Signal Transformer: Complex-Valued Attention and Meta-Learning for Signal Recognition. - Ying Peng; Yihong Dong; Muqiao Yang; Songtao Lu; Qingjiang Shi (ICASSP, 2024)

Joint Unsupervised and Supervised Training for Automatic Speech Recognition via Bilevel Optimization. - A. F. M. Saif; Xiaodong Cui; Han Shen; Songtao Lu; Brian Kingsbury; Tianyi Chen (ICASSP, 2024)

How Can Personalized Context Help? Exploring Joint Retrieval of Passage and Personalized Context. - Hui Wan; Hongkang Li; Songtao Lu; Xiaodong Cui; Marina Danilevsky (ICASSP, 2024)

PILOT: An $ mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation. - Zhuqing Liu; Xin Zhang 054; Jia Liu 002; Zhengyuan Zhu; Songtao Lu (ICLR, 2024)

SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning. - Shuai Zhang 015; Heshan Devaka Fernando; Miao Liu 001; Keerthiram Murugesan; Songtao Lu; Pin-Yu Chen; Tianyi Chen; Meng Wang 003 (ICML, 2024)

Distributed Bilevel Optimization with Communication Compression. - Yutong He; Jie Hu 022; Xinmeng Huang; Songtao Lu; Bin Wang; Kun Yuan 001 (ICML, 2024)

How Do Nonlinear Transformers Learn and Generalize in In-Context Learning? - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (ICML, 2024)

FADAS: Towards Federated Adaptive Asynchronous Optimization. - Yujia Wang; Shiqiang Wang; Songtao Lu; Jinghui Chen (ICML, 2024)

Federated Neuro-Symbolic Learning. - Pengwei Xing; Songtao Lu; Han Yu 001 (ICML, 2024)

M2ASR: Multilingual Multi-task Automatic Speech Recognition via Multi-objective Optimization. - A. F. M. Saif; Lisha Chen; Xiaodong Cui; Songtao Lu; Brian Kingsbury; Tianyi Chen (INTERSPEECH, 2024)

SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization. - Shuchen Zhu; Boao Kong; Songtao Lu; Xinmeng Huang; Kun Yuan 001 (NeurIPS, 2024)

Decentralized Bilevel Optimization over Graphs: Loopless Algorithmic Update and Transient Iteration Complexity. - Boao Kong; Shuchen Zhu; Songtao Lu; Xinmeng Huang; Kun Yuan 001 (CoRR, 2024)

Training Nonlinear Transformers for Efficient In-Context Learning: A Theoretical Learning and Generalization Analysis. - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (CoRR, 2024)

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. - Hongkang Li; Meng Wang 003; Songtao Lu; Xiaodong Cui; Pin-Yu Chen (CoRR, 2024)

Bilevel Joint Unsupervised and Supervised Training for Automatic Speech Recognition. - Xiaodong Cui; A. F. M. Saif; Songtao Lu; Lisha Chen; Tianyi Chen; Brian Kingsbury; George Saon (CoRR, 2024)

Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. - Zichong Li; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Yangyang Xu (Comput. Optim. Appl., 2024)

Correction to: Stochastic inexact augmented Lagrangian method for nonconvex expectation constrained optimization. - Zichong Li; Pin-Yu Chen; Sijia Liu 001; Songtao Lu; Yangyang Xu (Comput. Optim. Appl., 2024)

BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning. - Pengwei Xing; Songtao Lu; Lingfei Wu 001; Han Yu 001 (IEEE Trans. Big Data, 2024)

Q-function Decomposition with Intervention Semantics for Factored Action Spaces. - Junkyu Lee 001; Tian Gao; Elliot Nelson; Miao Liu 001; Debarun Bhattacharjya; Songtao Lu (AISTATS, 2025)

Epigraph Based Multilevel Optimization (EMO) for Enhancing Chain-of-Thought Reasoning Capabilities. - Songtao Lu; Yanna Ding; Lior Horesh; Jianxi Gao; Malik Magdon-Ismail (ICASSP, 2025)

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis. - Hongkang Li; Songtao Lu; Pin-Yu Chen; Xiaodong Cui; Meng Wang (ICLR, 2025)

DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity. - Zhen Qin; Zhuqing Liu; Songtao Lu; Yingbin Liang; Jia Liu 002 (ICLR, 2025)

Graph Neural Networks With Adaptive Structures. - Zepeng Zhang; Songtao Lu; Zengfeng Huang; Ziping Zhao 002 (IEEE J. Sel. Top. Signal Process., 2025)

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach. - Xinnan Zhang; Chenliang Li; Siliang Zeng; Jiaxiang Li; Zhongruo Wang; Kaixiang Lin; Songtao Lu; Alfredo García 001; Mingyi Hong 001 (CoRR, 2025)