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ORCIDMengdi Wang 0001
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2020 – today
- 2025
[c75]Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh, Tianrui Guan, Mengdi Wang, Ahmad Beirami, Furong Huang, Alvaro Velasquez, Dinesh Manocha, Amrit Singh Bedi:
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment. CVPR 2025: 25038-25049- [c74]Hui Yuan, Yifan Zeng, Yue Wu, Huazheng Wang, Mengdi Wang, Liu Leqi:
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement. ICLR 2025 - [c73]Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag, Soumya Suvra Ghosal, Jiahao Qiu, Mengdi Wang, Dinesh Manocha, Furong Huang, Alec Koppel, Sumitra Ganesh:
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment. ICLR 2025 - [c72]Yikuan Li, Chengsheng Mao, Kaixuan Huang, Hanyin Wang, Zheng Yu, Mengdi Wang, Yuan Luo:
Deep Reinforcement Learning for Efficient and Fair Allocation of Healthcare Resources. IJCAI 2025: 9790-9798 - [i95]Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, Mengdi Wang:
MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations. CoRR abs/2502.06453 (2025) - [i94]Souradip Chakraborty, Sujay Bhatt, Udari Madhushani Sehwag, Soumya Suvra Ghosal, Jiahao Qiu, Mengdi Wang, Dinesh Manocha, Furong Huang, Alec Koppel, Sumitra Ganesh:
Collab: Controlled Decoding using Mixture of Agents for LLM Alignment. CoRR abs/2503.21720 (2025) - [i93]Lawrence Liu, Inesh Chakrabarti, Yixiao Li, Mengdi Wang, Tuo Zhao, Lin F. Yang:
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models. CoRR abs/2504.14569 (2025) - [i92]Soumya Suvra Ghosal, Souradip Chakraborty, Avinash Reddy, Yifu Lu, Mengdi Wang, Dinesh Manocha, Furong Huang, Mohammad Ghavamzadeh, Amrit Singh Bedi:
Does Thinking More always Help? Understanding Test-Time Scaling in Reasoning Models. CoRR abs/2506.04210 (2025) - [i91]Jiahao Qiu, Xinzhe Juan, Yimin Wang, Ling Yang, Xuan Qi, Tongcheng Zhang, Jiacheng Guo, Yifu Lu, Zixin Yao, Hongru Wang, Shilong Liu, Xun Jiang, Liu Leqi, Mengdi Wang:
AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes. CoRR abs/2506.14728 (2025) - 2024
- [j32]Yitao Lu, Qian Chu, Zhen Li, Mengdi Wang, Robert A. Gatenby, Qingpeng Zhang:
Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer. Briefings Bioinform. 25(Supplement 1) (2024) - [j31]Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. J. Mach. Learn. Res. 25: 39:1-39:58 (2024) - [j30]Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang:
A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. Nat. Mac. Intell. 6(4): 449-460 (2024) - [j29]Yanyi Chu, Dan Yu, Yupeng Li, Kaixuan Huang, Yue Shen, Le Cong, Jason Zhang, Mengdi Wang:
Author Correction: A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions. Nat. Mac. Intell. 6(8): 988 (2024) - [j28]Jiandong Mu, Mengdi Wang, Feiwen Zhu, Jun Yang, Wei Lin, Wei Zhang:
Boosting the Convergence of Reinforcement Learning-Based Auto-Pruning Using Historical Data. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 43(2): 548-561 (2024) - [j27]Minshuo Chen, Jie Meng, Yu Bai, Yinyu Ye, H. Vincent Poor, Mengdi Wang:
Efficient Reinforcement Learning With Impaired Observability: Learning to Act With Delayed and Missing State Observations. IEEE Trans. Inf. Theory 70(10): 7251-7272 (2024) - [j26]Liang-Yong Xia, Yu Wu, Longfei Zhao, Leying Chen, Shiyi Zhang, Mengdi Wang, Jie Luo:
Redefining the Game: MVAE-DFDPnet's Low-Dimensional Embeddings for Superior Drug-Protein Interaction Predictions. IEEE J. Biomed. Health Informatics 28(7): 4317-4324 (2024) - [j25]Zheng Yu, Junyu Zhang, Zheng Wen, Andrea Tacchetti, Mengdi Wang, Ian Gemp:
Teamwork Reinforcement Learning With Concave Utilities. IEEE Trans. Mob. Comput. 23(5): 5709-5721 (2024) - [j24]Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song, Mengdi Wang, Huazheng Wang:
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models. Trans. Mach. Learn. Res. 2024 (2024) - [c71]Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang, Mengdi Wang:
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization. AAAI 2024: 14686-14694 - [c70]Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Peter Henderson, Mengdi Wang, Prateek Mittal:
Visual Adversarial Examples Jailbreak Aligned Large Language Models. AAAI 2024: 21527-21536 - [c69]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Huazheng Wang, Dinesh Manocha, Mengdi Wang, Furong Huang:
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback. ICLR 2024 - [c68]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. ICLR 2024 - [c67]Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Dinesh Manocha, Furong Huang, Amrit S. Bedi, Mengdi Wang:
MaxMin-RLHF: Alignment with Diverse Human Preferences. ICML 2024 - [c66]Alec Koppel, Sujay Bhatt, Jiacheng Guo, Joe Eappen, Mengdi Wang, Sumitra Ganesh:
Information-Directed Pessimism for Offline Reinforcement Learning. ICML 2024 - [c65]Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson:
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. ICML 2024 - [c64]Lei Zhao, Mengdi Wang, Yu Bai:
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective. ICML 2024 - [c63]Shuhua Yang, Hui Yuan, Xiaoying Zhang, Mengdi Wang, Hong Zhang, Huazheng Wang:
Conversational Dueling Bandits in Generalized Linear Models. KDD 2024: 3806-3817 - [c62]Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang:
A Theoretical Perspective for Speculative Decoding Algorithm. NeurIPS 2024 - [c61]Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha, Mengdi Wang, Amrit Singh Bedi, Furong Huang:
Transfer Q-star : Principled Decoding for LLM Alignment. NeurIPS 2024 - [c60]Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang:
Gradient Guidance for Diffusion Models: An Optimization Perspective. NeurIPS 2024 - [c59]Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup:
Offline Multitask Representation Learning for Reinforcement Learning. NeurIPS 2024 - [c58]Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette:
Fast Best-of-N Decoding via Speculative Rejection. NeurIPS 2024 - [c57]Zixuan Zhang, Kaiqi Zhang, Minshuo Chen, Yuma Takeda, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang:
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. NeurIPS 2024 - [i90]Jiahao Qiu, Hui Yuan, Jinghong Zhang, Wentao Chen, Huazheng Wang, Mengdi Wang:
Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization. CoRR abs/2401.06173 (2024) - [i89]Boyi Wei, Kaixuan Huang, Yangsibo Huang, Tinghao Xie, Xiangyu Qi, Mengzhou Xia, Prateek Mittal, Mengdi Wang, Peter Henderson:
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. CoRR abs/2402.05162 (2024) - [i88]Souradip Chakraborty, Jiahao Qiu, Hui Yuan, Alec Koppel, Furong Huang, Dinesh Manocha, Amrit Singh Bedi, Mengdi Wang:
MaxMin-RLHF: Towards Equitable Alignment of Large Language Models with Diverse Human Preferences. CoRR abs/2402.08925 (2024) - [i87]Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang:
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning. CoRR abs/2402.10810 (2024) - [i86]Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara:
Regularized DeepIV with Model Selection. CoRR abs/2403.04236 (2024) - [i85]Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup:
Offline Multitask Representation Learning for Reinforcement Learning. CoRR abs/2403.11574 (2024) - [i84]Hengyu Fu, Zhuoran Yang, Mengdi Wang, Minshuo Chen:
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory. CoRR abs/2403.11968 (2024) - [i83]Zihao Li, Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Yinyu Ye, Minshuo Chen, Mengdi Wang:
Diffusion Model for Data-Driven Black-Box Optimization. CoRR abs/2403.13219 (2024) - [i82]Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, Mengdi Wang:
Gradient Guidance for Diffusion Models: An Optimization Perspective. CoRR abs/2404.14743 (2024) - [i81]Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti, Jonas Geiping, Luxi He, Kaixuan Huang, Udari Madhushani, Vikash Sehwag, Weijia Shi, Boyi Wei, Tinghao Xie, Danqi Chen, Pin-Yu Chen, Jeffrey Ding, Ruoxi Jia, Jiaqi Ma, Arvind Narayanan, Weijie J. Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, Prateek Mittal:
AI Risk Management Should Incorporate Both Safety and Security. CoRR abs/2405.19524 (2024) - [i80]Souradip Chakraborty, Soumya Suvra Ghosal, Ming Yin, Dinesh Manocha, Mengdi Wang, Amrit Singh Bedi, Furong Huang:
Transfer Q Star: Principled Decoding for LLM Alignment. CoRR abs/2405.20495 (2024) - [i79]Mucong Ding, Souradip Chakraborty, Vibhu Agrawal, Zora Che, Alec Koppel, Mengdi Wang, Amrit S. Bedi, Furong Huang:
SAIL: Self-Improving Efficient Online Alignment of Large Language Models. CoRR abs/2406.15567 (2024) - [i78]Shuhua Yang, Hui Yuan, Xiaoying Zhang, Mengdi Wang, Hong Zhang, Huazheng Wang:
Conversational Dueling Bandits in Generalized Linear Models. CoRR abs/2407.18488 (2024) - [i77]Binshuai Wang, Qiwei Di, Ming Yin, Mengdi Wang, Quanquan Gu, Peng Wei:
Relative-Translation Invariant Wasserstein Distance. CoRR abs/2409.02416 (2024) - [i76]Bhrij Patel, Souradip Chakraborty, Wesley A. Suttle, Mengdi Wang, Amrit Singh Bedi, Dinesh Manocha:
AIME: AI System Optimization via Multiple LLM Evaluators. CoRR abs/2410.03131 (2024) - [i75]Hui Yuan, Yifan Zeng, Yue Wu, Huazheng Wang, Mengdi Wang, Liu Leqi:
A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement. CoRR abs/2410.13828 (2024) - [i74]Hanshi Sun, Momin Haider, Ruiqi Zhang, Huitao Yang, Jiahao Qiu, Ming Yin, Mengdi Wang, Peter L. Bartlett, Andrea Zanette:
Fast Best-of-N Decoding via Speculative Rejection. CoRR abs/2410.20290 (2024) - [i73]Ming Yin, Minshuo Chen, Kaixuan Huang, Mengdi Wang:
A Theoretical Perspective for Speculative Decoding Algorithm. CoRR abs/2411.00841 (2024) - [i72]Soumya Suvra Ghosal, Souradip Chakraborty, Vaibhav Singh, Tianrui Guan, Mengdi Wang, Ahmad Beirami, Furong Huang, Alvaro Velasquez, Dinesh Manocha, Amrit Singh Bedi:
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment. CoRR abs/2411.18688 (2024) - [i71]James Beetham, Souradip Chakraborty, Mengdi Wang, Furong Huang, Amrit Singh Bedi, Mubarak Shah:
LIAR: Leveraging Alignment (Best-of-N) to Jailbreak LLMs in Seconds. CoRR abs/2412.05232 (2024) - 2023
- [j23]Chengzhuo Ni, Yaqi Duan, Munther A. Dahleh, Mengdi Wang, Anru R. Zhang:
Learning Good State and Action Representations for Markov Decision Process via Tensor Decomposition. J. Mach. Learn. Res. 24: 115:1-115:53 (2023) - [j22]Zihao Li, Boyi Liu, Zhuoran Yang, Zhaoran Wang, Mengdi Wang:
Double Duality: Variational Primal-Dual Policy Optimization for Constrained Reinforcement Learning. J. Mach. Learn. Res. 24: 385:1-385:43 (2023) - [j21]Mingbao Lin, Yuxin Zhang, Yuchao Li, Bohong Chen, Fei Chao, Mengdi Wang, Shen Li, Yonghong Tian, Rongrong Ji:
1xN Pattern for Pruning Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 3999-4008 (2023) - [j20]Junyu Zhang, Mengdi Wang, Mingyi Hong, Shuzhong Zhang:
Primal-Dual First-Order Methods for Affinely Constrained Multi-block Saddle Point Problems. SIAM J. Optim. 33(2): 1035-1060 (2023) - [c56]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. AISTATS 2023: 3230-3269 - [c55]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. COLT 2023: 2114-2187 - [c54]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Zihan Ding, Chi Jin, Mengdi Wang:
Representation Learning for Low-rank General-sum Markov Games. ICLR 2023 - [c53]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. ICLR 2023 - [c52]Zheng Yu, Yikuan Li, Joseph C. Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang:
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis. ICLR 2023 - [c51]Souradip Chakraborty, Amrit S. Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING : Stein Information Directed Exploration for Model-Based Reinforcement Learning. ICML 2023: 3949-3978 - [c50]Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang:
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. NeurIPS 2023 - [c49]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yian Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. NeurIPS 2023 - [c48]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. NeurIPS 2023 - [c47]Zeyu Zhang, Yi Su, Hui Yuan, Yiran Wu, Rishab Balasubramanian, Qingyun Wu, Huazheng Wang, Mengdi Wang:
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective. NeurIPS 2023 - [i70]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Furong Huang, Dinesh Manocha:
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning. CoRR abs/2301.12038 (2023) - [i69]Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang:
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. CoRR abs/2302.07194 (2023) - [i68]Zheng Yu, Yikuan Li, Joseph C. Kim, Kaixuan Huang, Yuan Luo, Mengdi Wang:
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis. CoRR abs/2302.10261 (2023) - [i67]Zichen Wang, Rishab Balasubramanian, Hui Yuan, Chenyu Song, Mengdi Wang, Huazheng Wang:
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models. CoRR abs/2305.19218 (2023) - [i66]Minshuo Chen, Yu Bai, H. Vincent Poor, Mengdi Wang:
Efficient RL with Impaired Observability: Learning to Act with Delayed and Missing State Observations. CoRR abs/2306.01243 (2023) - [i65]Zeyu Zhang, Yi Su, Hui Yuan, Yiran Wu, Rishab Balasubramanian, Qingyun Wu, Huazheng Wang, Mengdi Wang:
Unified Off-Policy Learning to Rank: a Reinforcement Learning Perspective. CoRR abs/2306.07528 (2023) - [i64]Xiangyu Qi, Kaixuan Huang, Ashwinee Panda, Mengdi Wang, Prateek Mittal:
Visual Adversarial Examples Jailbreak Large Language Models. CoRR abs/2306.13213 (2023) - [i63]Kaiqi Zhang, Zixuan Zhang, Minshuo Chen, Mengdi Wang, Tuo Zhao, Yu-Xiang Wang:
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks. CoRR abs/2307.01649 (2023) - [i62]Jiacheng Guo, Minshuo Chen, Huan Wang, Caiming Xiong, Mengdi Wang, Yu Bai:
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight. CoRR abs/2307.02884 (2023) - [i61]Hui Yuan, Kaixuan Huang, Chengzhuo Ni, Minshuo Chen, Mengdi Wang:
Reward-Directed Conditional Diffusion: Provable Distribution Estimation and Reward Improvement. CoRR abs/2307.07055 (2023) - [i60]Xiang Ji, Huazheng Wang, Minshuo Chen, Tuo Zhao, Mengdi Wang:
Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems. CoRR abs/2307.12975 (2023) - [i59]Souradip Chakraborty, Amrit Singh Bedi, Alec Koppel, Dinesh Manocha, Huazheng Wang, Furong Huang, Mengdi Wang:
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL. CoRR abs/2308.02585 (2023) - [i58]Yikuan Li, Chengsheng Mao, Kaixuan Huang, Hanyin Wang, Zheng Yu, Mengdi Wang, Yuan Luo:
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources. CoRR abs/2309.08560 (2023) - [i57]Zhenghao Xu, Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds. CoRR abs/2309.13915 (2023) - [i56]Shuoguang Yang, Xuezhou Zhang, Mengdi Wang:
Federated Multi-Level Optimization over Decentralized Networks. CoRR abs/2310.06217 (2023) - [i55]Zihao Li, Xiang Ji, Minshuo Chen, Mengdi Wang:
Sample Complexity of Preference-Based Nonparametric Off-Policy Evaluation with Deep Networks. CoRR abs/2310.10556 (2023) - [i54]Nikki Lijing Kuang, Ming Yin, Mengdi Wang, Yu-Xiang Wang, Yi-An Ma:
Posterior Sampling with Delayed Feedback for Reinforcement Learning with Linear Function Approximation. CoRR abs/2310.18919 (2023) - [i53]Lei Zhao, Mengdi Wang, Yu Bai:
Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? CoRR abs/2312.00054 (2023) - 2022
- [j19]Ziwei Zhu, Xudong Li, Mengdi Wang, Anru Zhang:
Learning Markov Models Via Low-Rank Optimization. Oper. Res. 70(4): 2384-2398 (2022) - [j18]Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu, Mengdi Wang, Vijay Vittal, P. R. Kumar, Srinivas Shakkottai, Yi Cui:
Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality. Patterns 3(12): 100640 (2022) - [c46]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic. AAAI 2022: 9031-9039 - [c45]Chenyu Wang, Joseph C. Kim, Le Cong, Mengdi Wang:
Neural Bandits for Protein Sequence Optimization. CISS 2022: 188-193 - [c44]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. ICLR 2022 - [c43]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. ICML 2022: 26517-26547 - [c42]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. NeurIPS 2022 - [c41]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline stochastic shortest path: Learning, evaluation and towards optimality. UAI 2022: 2278-2288 - [i52]Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun:
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach. CoRR abs/2202.00063 (2022) - [i51]Ming Yin, Yaqi Duan, Mengdi Wang, Yu-Xiang Wang:
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism. CoRR abs/2203.05804 (2022) - [i50]Yuchao Li, Fuli Luo, Chuanqi Tan, Mengdi Wang, Songfang Huang, Shen Li, Junjie Bai:
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning. CoRR abs/2205.11005 (2022) - [i49]Alekh Agarwal, Yuda Song, Wen Sun, Kaiwen Wang, Mengdi Wang, Xuezhou Zhang:
Provable Benefits of Representational Transfer in Reinforcement Learning. CoRR abs/2205.14571 (2022) - [i48]Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, Xiaojin Zhu:
Byzantine-Robust Online and Offline Distributed Reinforcement Learning. CoRR abs/2206.00165 (2022) - [i47]Hui Yuan, Chengzhuo Ni, Huazheng Wang, Xuezhou Zhang, Le Cong, Csaba Szepesvári, Mengdi Wang:
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization. CoRR abs/2206.02092 (2022) - [i46]Xiang Ji, Minshuo Chen, Mengdi Wang, Tuo Zhao:
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks. CoRR abs/2206.02887 (2022) - [i45]Chuanhao Li, Huazheng Wang, Mengdi Wang, Hongning Wang:
Communication Efficient Distributed Learning for Kernelized Contextual Bandits. CoRR abs/2206.04835 (2022) - [i44]Ming Yin, Wenjing Chen, Mengdi Wang, Yu-Xiang Wang:
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality. CoRR abs/2206.04921 (2022) - [i43]Kaixuan Huang, Yu Wu, Xuezhou Zhang, Shenyinying Tu, Qingyun Wu, Mengdi Wang, Huazheng Wang:
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization. CoRR abs/2206.14846 (2022) - [i42]Ming Yin, Mengdi Wang, Yu-Xiang Wang:
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient. CoRR abs/2210.00750 (2022) - [i41]Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin, Mengdi Wang:
Representation Learning for General-sum Low-rank Markov Games. CoRR abs/2210.16976 (2022) - [i40]Le Xie, Tong Huang, Xiangtian Zheng, Yan Liu, Mengdi Wang, Vijay Vittal, P. R. Kumar, Srinivas Shakkottai, Yi Cui:
Energy System Digitization in the Era of AI: A Three-Layered Approach towards Carbon Neutrality. CoRR abs/2211.04584 (2022) - [i39]Jinghan Wang, Mengdi Wang, Lin F. Yang:
Near Sample-Optimal Reduction-based Policy Learning for Average Reward MDP. CoRR abs/2212.00603 (2022) - 2021
- [j17]Yue Xu, Zengde Deng, Mengdi Wang, Wenjun Xu, Anthony Man-Cho So, Shuguang Cui:
Voting-Based Multiagent Reinforcement Learning for Intelligent IoT. IEEE Internet Things J. 8(4): 2681-2693 (2021) - [j16]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. IEEE J. Sel. Areas Inf. Theory 2(2): 611-626 (2021) - [c40]Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. AISTATS 2021: 316-324 - [c39]Junyu Zhang, Mingyi Hong, Mengdi Wang, Shuzhong Zhang:
Generalization Bounds for Stochastic Saddle Point Problems. AISTATS 2021: 568-576 - [c38]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures. ACC 2021: 894-901 - [c37]Amrit Singh Bedi, Alec Koppel, Mengdi Wang, Junyu Zhang:
Intermittent Communications in Decentralized Shadow Reward Actor-Critic. CDC 2021: 2613-2620 - [c36]Yuchao Li, Shaohui Lin, Jianzhuang Liu, Qixiang Ye, Mengdi Wang, Fei Chao, Fan Yang, Jincheng Ma, Qi Tian, Rongrong Ji:
Towards Compact CNNs via Collaborative Compression. CVPR 2021: 6438-6447 - [c35]Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. ICML 2021: 4063-4073 - [c34]Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang:
Bootstrapping Fitted Q-Evaluation for Off-Policy Inference. ICML 2021: 4074-4084 - [c33]Chengzhuo Ni, Anru R. Zhang, Yaqi Duan, Mengdi Wang:
Learning Good State and Action Representations via Tensor Decomposition. ISIT 2021: 1682-1687 - [c32]Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang:
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. NeurIPS 2021: 2228-2240 - [i38]Botao Hao, Xiang Ji, Yaqi Duan, Hao Lu, Csaba Szepesvári, Mengdi Wang:
Bootstrapping Statistical Inference for Off-Policy Evaluation. CoRR abs/2102.03607 (2021) - [i37]Junyu Zhang, Chengzhuo Ni, Zheng Yu, Csaba Szepesvári, Mengdi Wang:
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method. CoRR abs/2102.08607 (2021) - [i36]Chengzhuo Ni, Anru Zhang, Yaqi Duan, Mengdi Wang:
Learning Good State and Action Representations via Tensor Decomposition. CoRR abs/2105.01136 (2021) - [i35]Yuchao Li, Shaohui Lin, Jianzhuang Liu, Qixiang Ye, Mengdi Wang, Fei Chao, Fan Yang, Jincheng Ma, Qi Tian, Rongrong Ji:
Towards Compact CNNs via Collaborative Compression. CoRR abs/2105.11228 (2021) - [i34]Mingbao Lin, Yuchao Li, Yuxin Zhang, Bohong Chen, Fei Chao, Mengdi Wang, Shen Li, Jun Yang, Rongrong Ji:
1×N Block Pattern for Network Sparsity. CoRR abs/2105.14713 (2021) - [i33]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. CoRR abs/2106.00543 (2021) - [i32]Shaokun Zhang, Xiawu Zheng, Chenyi Yang, Yuchao Li, Yan Wang, Fei Chao, Mengdi Wang, Shen Li, Jun Yang, Rongrong Ji:
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient. CoRR abs/2106.02435 (2021) - [i31]Amrit Singh Bedi, Anjaly Parayil, Junyu Zhang, Mengdi Wang, Alec Koppel:
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control. CoRR abs/2106.08414 (2021) - [i30]Jiandong Mu, Mengdi Wang, Feiwen Zhu, Jun Yang, Wei Lin, Wei Zhang:
Boosting the Convergence of Reinforcement Learning-based Auto-pruning Using Historical Data. CoRR abs/2107.08815 (2021) - 2020
- [j15]Mengdi Wang:
Randomized Linear Programming Solves the Markov Decision Problem in Nearly Linear (Sometimes Sublinear) Time. Math. Oper. Res. 45(2): 517-546 (2020) - [j14]Saeed Ghadimi, Andrzej Ruszczynski, Mengdi Wang:
A Single Timescale Stochastic Approximation Method for Nested Stochastic Optimization. SIAM J. Optim. 30(1): 960-979 (2020) - [j13]Yaqi Duan, Mengdi Wang, Zaiwen Wen, Yaxiang Yuan:
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains. SIAM J. Matrix Anal. Appl. 41(1): 244-278 (2020) - [j12]Anru Zhang, Mengdi Wang:
Spectral State Compression of Markov Processes. IEEE Trans. Inf. Theory 66(5): 3202-3231 (2020) - [c31]Yingyu Liang, Zhao Song, Mengdi Wang, Lin Yang, Xin Yang:
Sketching Transformed Matrices with Applications to Natural Language Processing. AISTATS 2020: 467-481 - [c30]Aaron Sidford, Mengdi Wang, Lin Yang, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. AISTATS 2020: 2992-3002 - [c29]Tianyi Lin, Chengyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Sample Complexity for Stochastic Compositional Variance Reduced Gradient. ACC 2020: 126-131 - [c28]Jiandong Mu, Mengdi Wang, Lanbo Li, Jun Yang, Wei Lin, Wei Zhang:
A History-Based Auto-Tuning Framework for Fast and High-Performance DNN Design on GPU. DAC 2020: 1-6 - [c27]Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin Yang:
Model-Based Reinforcement Learning with Value-Targeted Regression. ICML 2020: 463-474 - [c26]Lin Yang, Mengdi Wang:
Reinforcement Learning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. ICML 2020: 10746-10756 - [c25]Zeyu Jia, Lin Yang, Csaba Szepesvári, Mengdi Wang:
Model-Based Reinforcement Learning with Value-Targeted Regression. L4DC 2020: 666-686 - [c24]Xiaodong Yi, Ziyue Luo, Chen Meng, Mengdi Wang, Guoping Long, Chuan Wu, Jun Yang, Wei Lin:
Fast Training of Deep Learning Models over Multiple GPUs. Middleware 2020: 105-118 - [c23]Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu:
Generalized Leverage Score Sampling for Neural Networks. NeurIPS 2020 - [c22]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations. NeurIPS 2020 - [c21]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. NeurIPS 2020 - [i29]Yaqi Duan, Mengdi Wang:
Minimax-Optimal Off-Policy Evaluation with Linear Function Approximation. CoRR abs/2002.09516 (2020) - [i28]Yingyu Liang, Zhao Song, Mengdi Wang, Lin F. Yang, Xin Yang:
Sketching Transformed Matrices with Applications to Natural Language Processing. CoRR abs/2002.09812 (2020) - [i27]Junyu Zhang, Amrit Singh Bedi, Mengdi Wang, Alec Koppel:
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. CoRR abs/2002.12475 (2020) - [i26]Alex Ayoub, Zeyu Jia, Csaba Szepesvári, Mengdi Wang, Lin F. Yang:
Model-Based Reinforcement Learning with Value-Targeted Regression. CoRR abs/2006.01107 (2020) - [i25]Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao:
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. CoRR abs/2006.15261 (2020) - [i24]Junyu Zhang, Alec Koppel, Amrit Singh Bedi, Csaba Szepesvári, Mengdi Wang:
Variational Policy Gradient Method for Reinforcement Learning with General Utilities. CoRR abs/2007.02151 (2020) - [i23]Jason D. Lee, Ruoqi Shen, Zhao Song, Mengdi Wang, Zheng Yu:
Generalized Leverage Score Sampling for Neural Networks. CoRR abs/2009.09829 (2020) - [i22]Botao Hao, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Online Sparse Reinforcement Learning. CoRR abs/2011.04018 (2020) - [i21]Botao Hao, Yaqi Duan, Tor Lattimore, Csaba Szepesvári, Mengdi Wang:
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient. CoRR abs/2011.04019 (2020) - [i20]Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael I. Jordan:
Bridging Exploration and General Function Approximation in Reinforcement Learning: Provably Efficient Kernel and Neural Value Iterations. CoRR abs/2011.04622 (2020)
2010 – 2019
- 2019
- [j11]Yichen Chen, Yinyu Ye, Mengdi Wang:
Approximation Hardness for A Class of Sparse Optimization Problems. J. Mach. Learn. Res. 20: 38:1-38:27 (2019) - [j10]Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao:
Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python. J. Mach. Learn. Res. 20: 44:1-44:5 (2019) - [j9]Ethan X. Fang, Han Liu, Mengdi Wang:
Blessing of massive scale: spatial graphical model estimation with a total cardinality constraint approach. Math. Program. 176(1-2): 175-205 (2019) - [j8]Shuoguang Yang, Mengdi Wang, Ethan X. Fang:
Multilevel Stochastic Gradient Methods for Nested Composition Optimization. SIAM J. Optim. 29(1): 616-659 (2019) - [c20]Chengzhuo Ni, Lin F. Yang, Mengdi Wang:
Learning to Control in Metric Space with Optimal Regret. Allerton 2019: 726-733 - [c19]Lin Yang, Mengdi Wang:
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features. ICML 2019: 6995-7004 - [c18]Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia:
Characterizing Deep Learning Training Workloads on Alibaba-PAI. IISWC 2019: 189-202 - [c17]Chengzhuo Ni, Mengdi Wang:
Maximum Likelihood Tensor Decomposition of Markov Decision Process. ISIT 2019: 3062-3066 - [c16]Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang:
Learning low-dimensional state embeddings and metastable clusters from time series data. NeurIPS 2019: 4563-4572 - [c15]Lin F. Yang, Zheng Yu, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Online Factorization and Partition of Complex Networks by Random Walk. UAI 2019: 820-830 - [i19]Lin F. Yang, Mengdi Wang:
Sample-Optimal Parametric Q-Learning with Linear Transition Models. CoRR abs/1902.04779 (2019) - [i18]Lin F. Yang, Chengzhuo Ni, Mengdi Wang:
Learning to Control in Metric Space with Optimal Regret. CoRR abs/1905.01576 (2019) - [i17]Lin F. Yang, Mengdi Wang:
Reinforcement Leaning in Feature Space: Matrix Bandit, Kernels, and Regret Bound. CoRR abs/1905.10389 (2019) - [i16]Yifan Sun, Yaqi Duan, Hao Gong, Mengdi Wang:
Learning low-dimensional state embeddings and metastable clusters from time series data. CoRR abs/1906.00302 (2019) - [i15]Zeyu Jia, Lin F. Yang, Mengdi Wang:
Feature-Based Q-Learning for Two-Player Stochastic Games. CoRR abs/1906.00423 (2019) - [i14]Yue Xu, Zengde Deng, Mengdi Wang, Wenjun Xu, Anthony Man-Cho So, Shuguang Cui:
Voting-Based Multi-Agent Reinforcement Learning. CoRR abs/1907.01385 (2019) - [i13]Aaron Sidford, Mengdi Wang, Lin F. Yang, Yinyu Ye:
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity. CoRR abs/1908.11071 (2019) - [i12]Mengdi Wang, Chen Meng, Guoping Long, Chuan Wu, Jun Yang, Wei Lin, Yangqing Jia:
Characterizing Deep Learning Training Workloads on Alibaba-PAI. CoRR abs/1910.05930 (2019) - [i11]Simon S. Du, Ruosong Wang, Mengdi Wang, Lin F. Yang:
Continuous Control with Contexts, Provably. CoRR abs/1910.13614 (2019) - 2018
- [j7]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Near-optimal stochastic approximation for online principal component estimation. Math. Program. 167(1): 75-97 (2018) - [c14]Jason Ge, Zhaoran Wang, Mengdi Wang, Han Liu:
Minimax-Optimal Privacy-Preserving Sparse PCA in Distributed Systems. AISTATS 2018: 1589-1598 - [c13]Qing Zhang, Mengru Zhang, Mengdi Wang, Wanchen Sui, Chen Meng, Jun Yang, Weidan Kong, Xiaoyuan Cui, Wei Lin:
Efficient Deep Learning Inference Based on Model Compression. CVPR Workshops 2018: 1695-1702 - [c12]Xudong Li, Mengdi Wang, Anru Zhang:
Estimation of Markov Chain via Rank-constrained Likelihood. ICML 2018: 3039-3048 - [c11]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. NeurIPS 2018: 3500-3510 - [c10]Aaron Sidford, Mengdi Wang, Xian Wu, Lin Yang, Yinyu Ye:
Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model. NeurIPS 2018: 5192-5202 - [c9]Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye:
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. SODA 2018: 770-787 - [i10]Anru Zhang, Mengdi Wang:
State Compression of Markov Processes via Empirical Low-Rank Estimation. CoRR abs/1802.02920 (2018) - [i9]Minshuo Chen, Lin Yang, Mengdi Wang, Tuo Zhao:
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization. CoRR abs/1803.02312 (2018) - [i8]Xudong Li, Mengdi Wang, Anru Zhang:
Estimation of Markov Chain via Rank-constrained Likelihood. CoRR abs/1804.00795 (2018) - [i7]Tianyi Lin, Chenyou Fan, Mengdi Wang, Michael I. Jordan:
Improved Oracle Complexity for Stochastic Compositional Variance Reduced Gradient. CoRR abs/1806.00458 (2018) - [i6]Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. CoRR abs/1808.09645 (2018) - [i5]Yaqi Duan, Zheng Tracy Ke, Mengdi Wang:
State Aggregation Learning from Markov Transition Data. CoRR abs/1811.02619 (2018) - [i4]Mengdi Wang, Qing Zhang, Jun Yang, Xiaoyuan Cui, Wei Lin:
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks. CoRR abs/1811.08589 (2018) - 2017
- [j6]Mengdi Wang, Ethan X. Fang, Han Liu:
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions. Math. Program. 161(1-2): 419-449 (2017) - [j5]Mengdi Wang:
Vanishing Price of Decentralization in Large Coordinative Nonconvex Optimization. SIAM J. Optim. 27(3): 1977-2009 (2017) - [c8]Xiangru Lian, Mengdi Wang, Ji Liu:
Finite-sum Composition Optimization via Variance Reduced Gradient Descent. AISTATS 2017: 1159-1167 - [c7]Yichen Chen, Dongdong Ge, Mengdi Wang, Zizhuo Wang, Yinyu Ye, Hao Yin:
Strong NP-Hardness for Sparse Optimization with Concave Penalty Functions. ICML 2017: 740-747 - [c6]Chris Junchi Li, Mengdi Wang, Tong Zhang:
Diffusion Approximations for Online Principal Component Estimation and Global Convergence. NIPS 2017: 645-655 - [i3]Lin F. Yang, Vladimir Braverman, Tuo Zhao, Mengdi Wang:
Dynamic Factorization and Partition of Complex Networks. CoRR abs/1705.07881 (2017) - [i2]Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye:
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes. CoRR abs/1710.09988 (2017) - 2016
- [j4]Mengdi Wang, Dimitri P. Bertsekas:
Stochastic First-Order Methods with Random Constraint Projection. SIAM J. Optim. 26(1): 681-717 (2016) - [c5]Mengdi Wang, Yichen Chen:
An online primal-dual method for discounted Markov decision processes. CDC 2016: 4516-4521 - [c4]Mengdi Wang, Ji Liu:
A stochastic compositional gradient method using Markov samples. WSC 2016: 702-713 - 2015
- [j3]Xiaohan Wang, Mengdi Wang, Yuantao Gu:
A Distributed Tracking Algorithm for Reconstruction of Graph Signals. IEEE J. Sel. Top. Signal Process. 9(4): 728-740 (2015) - [j2]Mengdi Wang, Dimitri P. Bertsekas:
Incremental constraint projection methods for variational inequalities. Math. Program. 150(2): 321-363 (2015) - [c3]Jialin Liu, Yuantao Gu, Mengdi Wang:
Averaging random projection: A fast online solution for large-scale constrained stochastic optimization. ICASSP 2015: 3586-3590 - [i1]Mengdi Wang, Yichen Chen, Jialin Liu, Yuantao Gu:
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints. CoRR abs/1511.03760 (2015) - 2014
- [j1]Mengdi Wang, Dimitri P. Bertsekas:
Stabilization of Stochastic Iterative Methods for Singular and Nearly Singular Linear Systems. Math. Oper. Res. 39(1): 1-30 (2014) - [c2]Yuantao Gu, Mengdi Wang:
Learning distributed jointly sparse systems by collaborative LMS. ICASSP 2014: 7228-7232 - [c1]Mengdi Wang, Yunjian Xu, Yuntao Gu:
Multi-task nonconvex optimization with total budget constraint: A distributed algorithm using Monte Carlo estimates. DSP 2014: 793-796
Coauthor Index
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