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ORCIDWei Chen 0034
◁ ▷ joint publications with coauthor community group 1
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Publications
- 2025
[i82]Yu Chen, Siwei Wang, Longbo Huang, Wei Chen:
Continuous K-Max Bandits. CoRR abs/2502.13467 (2025)- [i81]Mingkun Zhang, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng:
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification. CoRR abs/2502.18176 (2025) - 2024
- [j12]Yubao Tang
, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng:
Listwise Generative Retrieval Models via a Sequential Learning Process. ACM Trans. Inf. Syst. 42(5): 133:1-133:31 (2024) - [c66]Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen, Maarten de Rijke, Jiafeng Guo, Xueqi Cheng:
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off. AAAI 2024: 8832-8840 - [c65]Mingkun Zhang, Jianing Li, Wei Chen, Jiafeng Guo, Xueqi Cheng:
Classifier Guidance Enhances Diffusion-Based Adversarial Purification by Preserving Predictive Information. ECAI 2024: 2234-2241 - [c64]Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang:
Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering. EMNLP (Findings) 2024: 7598-7610 - [c63]Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng, Ruoyu Sun, Zhi-Ming Ma, Tie-Yan Liu, Zhi-Quan Luo, Wei Chen:
Provable Adaptivity of Adam under Non-uniform Smoothness. KDD 2024: 2960-2969 - [c62]Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng:
Generative Retrieval Meets Multi-Graded Relevance. NeurIPS 2024 - [c61]Mingkun Zhang, Keping Bi, Wei Chen, Quanrun Chen, Jiafeng Guo, Xueqi Cheng:
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense. NeurIPS 2024 - [i80]Lu Chen, Wei Huang, Ruqing Zhang, Wei Chen, Jiafeng Guo, Xueqi Cheng:
A Unified Causal View of Instruction Tuning. CoRR abs/2402.06220 (2024) - [i79]Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng:
Listwise Generative Retrieval Models via a Sequential Learning Process. CoRR abs/2403.12499 (2024) - [i78]Bohan Wang, Huishuai Zhang, Qi Meng, Ruoyu Sun, Zhi-Ming Ma, Wei Chen:
On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond. CoRR abs/2403.15146 (2024) - [i77]Yixin Ji, Yang Xiang, Juntao Li, Wei Chen, Zhongyi Liu, Kehai Chen, Min Zhang:
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization. CoRR abs/2405.10616 (2024) - [i76]Yuchen Wen, Keping Bi, Wei Chen, Jiafeng Guo, Xueqi Cheng:
Evaluating Implicit Bias in Large Language Models by Attacking From a Psychometric Perspective. CoRR abs/2406.14023 (2024) - [i75]Mingkun Zhang, Jianing Li, Wei Chen, Jiafeng Guo, Xueqi Cheng:
Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information. CoRR abs/2408.05900 (2024) - [i74]Yubao Tang, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Xueqi Cheng:
Generative Retrieval Meets Multi-Graded Relevance. CoRR abs/2409.18409 (2024) - [i73]Mingkun Zhang, Keping Bi, Wei Chen, Quanrun Chen, Jiafeng Guo, Xueqi Cheng:
CausalDiff: Causality-Inspired Disentanglement via Diffusion Model for Adversarial Defense. CoRR abs/2410.23091 (2024) - 2023
- [j11]Shiqi Gong, Qi Meng, Yue Wang, Lijun Wu, Wei Chen, Zhiming Ma, Tie-Yan Liu:
Incorporating NODE with pre-trained neural differential operator for learning dynamics. Neurocomputing 528: 48-58 (2023) - [c60]Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng:
Inducing Causal Structure for Abstractive Text Summarization. CIKM 2023: 213-223 - [c59]Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Continual Learning for Generative Retrieval over Dynamic Corpora. CIKM 2023: 306-315 - [c58]Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method. CIKM 2023: 1647-1656 - [c57]Bohan Wang, Huishuai Zhang, Zhiming Ma, Wei Chen:
Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions. COLT 2023: 161-190 - [c56]Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang:
Collaborative Pure Exploration in Kernel Bandit. ICLR 2023 - [c55]Bohan Wang, Jingwen Fu, Huishuai Zhang, Nanning Zheng, Wei Chen:
Closing the gap between the upper bound and lower bound of Adam's iteration complexity. NeurIPS 2023 - [c54]Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models. SIGIR 2023: 1700-1709 - [i72]Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Topic-oriented Adversarial Attacks against Black-box Neural Ranking Models. CoRR abs/2304.14867 (2023) - [i71]Bohan Wang, Huishuai Zhang, Zhi-Ming Ma, Wei Chen:
Convergence of AdaGrad for Non-convex Objectives: Simple Proofs and Relaxed Assumptions. CoRR abs/2305.18471 (2023) - [i70]Jingwen Fu, Bohan Wang, Huishuai Zhang, Zhizheng Zhang, Wei Chen, Nanning Zheng:
When and Why Momentum Accelerates SGD: An Empirical Study. CoRR abs/2306.09000 (2023) - [i69]Wei Chen, Weitao Du, Zhi-Ming Ma, Qi Meng:
Power-law Dynamic arising from machine learning. CoRR abs/2306.09624 (2023) - [i68]Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Xueqi Cheng:
On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective. CoRR abs/2306.12756 (2023) - [i67]Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method. CoRR abs/2308.09861 (2023) - [i66]Lu Chen, Ruqing Zhang, Wei Huang, Wei Chen, Jiafeng Guo, Xueqi Cheng:
Inducing Causal Structure for Abstractive Text Summarization. CoRR abs/2308.12888 (2023) - [i65]Jiangui Chen, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke, Wei Chen, Yixing Fan, Xueqi Cheng:
Continual Learning for Generative Retrieval over Dynamic Corpora. CoRR abs/2308.14968 (2023) - [i64]Bohan Wang, Jingwen Fu, Huishuai Zhang, Nanning Zheng, Wei Chen:
Closing the Gap Between the Upper Bound and the Lower Bound of Adam's Iteration Complexity. CoRR abs/2310.17998 (2023) - [i63]Yinqiong Cai, Yixing Fan, Keping Bi, Jiafeng Guo, Wei Chen, Ruqing Zhang, Xueqi Cheng:
CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval. CoRR abs/2311.02834 (2023) - [i62]Yu-An Liu, Ruqing Zhang, Mingkun Zhang, Wei Chen, Maarten de Rijke, Jiafeng Guo, Xueqi Cheng:
Perturbation-Invariant Adversarial Training for Neural Ranking Models: Improving the Effectiveness-Robustness Trade-Off. CoRR abs/2312.10329 (2023) - 2022
- [j10]Juanping Zhu, Qi Meng, Wei Chen, Yue Wang, Zhiming Ma:
Constructing the Basis Path Set by Eliminating the Path Dependency. J. Syst. Sci. Complex. 35(5): 1944-1962 (2022) - [j9]Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, Tie-Yan Liu:
Stabilize deep ResNet with a sharp scaling factor τ. Mach. Learn. 111(9): 3359-3392 (2022) - [c53]Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng:
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models. CIKM 2022: 2128-2137 - [c52]Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu:
Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart. CVPR 2022: 15202-15212 - [c51]Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu:
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Dependent Adaptive Prior. ICLR 2022 - [c50]Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu:
Gradient Information Matters in Policy Optimization by Back-propagating through Model. ICLR 2022 - [c49]Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Nanning Zheng, Bin Shao, Tie-Yan Liu:
SE(3) Equivariant Graph Neural Networks with Complete Local Frames. ICML 2022: 5583-5608 - [c48]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Availability Attacks Create Shortcuts. KDD 2022: 2367-2376 - [c47]Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. NeurIPS 2022 - [c46]Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Does Momentum Change the Implicit Regularization on Separable Data? NeurIPS 2022 - [i61]Yihan Du, Wei Chen:
Branching Reinforcement Learning. CoRR abs/2202.07995 (2022) - [i60]Peiyan Hu, Qi Meng, Bingguang Chen, Shiqi Gong, Yue Wang, Wei Chen, Rongchan Zhu, Zhi-Ming Ma, Tie-Yan Liu:
Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs. CoRR abs/2204.06255 (2022) - [i59]Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu:
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. CoRR abs/2205.12418 (2022) - [i58]Xiaodong Yang, Huishuai Zhang, Wei Chen, Tie-Yan Liu:
Normalized/Clipped SGD with Perturbation for Differentially Private Non-Convex Optimization. CoRR abs/2206.13033 (2022) - [i57]Bohan Wang, Yushun Zhang, Huishuai Zhang, Qi Meng, Zhi-Ming Ma, Tie-Yan Liu, Wei Chen:
Provable Adaptivity in Adam. CoRR abs/2208.09900 (2022) - [i56]Chen Wu, Ruqing Zhang, Jiafeng Guo, Wei Chen, Yixing Fan, Maarten de Rijke, Xueqi Cheng:
Certified Robustness to Word Substitution Ranking Attack for Neural Ranking Models. CoRR abs/2209.06691 (2022) - 2021
- [j8]Juanping Zhu, Qi Meng, Wei Chen, Zhiming Ma:
Interpreting the Basis Path Set in Neural Networks. J. Syst. Sci. Complex. 34(6): 2155-2167 (2021) - [c45]Yihan Du, Yuko Kuroki, Wei Chen:
Combinatorial Pure Exploration with Full-Bandit or Partial Linear Feedback. AAAI 2021: 7262-7270 - [c44]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
How Does Data Augmentation Affect Privacy in Machine Learning? AAAI 2021: 10746-10753 - [c43]Hao Zhou, Minlie Huang, Yong Liu, Wei Chen, Xiaoyan Zhu:
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning. EMNLP (1) 2021: 2383-2395 - [c42]Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu:
Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning. ICLR 2021 - [c41]Bohan Wang, Qi Meng, Wei Chen, Tie-Yan Liu:
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks. ICML 2021: 10849-10858 - [c40]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Large Scale Private Learning via Low-rank Reparametrization. ICML 2021: 12208-12218 - [c39]Chang Liu, Xinwei Sun, Jindong Wang, Haoyue Tang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu:
Learning Causal Semantic Representation for Out-of-Distribution Prediction. NeurIPS 2021: 6155-6170 - [c38]Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu:
R-Drop: Regularized Dropout for Neural Networks. NeurIPS 2021: 10890-10905 - [c37]Xinwei Sun, Botong Wu, Xiangyu Zheng, Chang Liu, Wei Chen, Tao Qin, Tie-Yan Liu:
Recovering Latent Causal Factor for Generalization to Distributional Shifts. NeurIPS 2021: 16846-16859 - [c36]Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu:
Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD. NeurIPS 2021: 26080-26090 - [c35]Xufang Luo, Qi Meng, Wei Chen, Yunhong Wang, Tie-Yan Liu:
Path-BN: Towards effective batch normalization in the Path Space for ReLU networks. UAI 2021: 834-843 - [i55]Mingyang Yi, Qi Meng, Wei Chen, Zhi-Ming Ma:
Towards Accelerating Training of Batch Normalization: A Manifold Perspective. CoRR abs/2101.02916 (2021) - [i54]Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
BN-invariant sharpness regularizes the training model to better generalization. CoRR abs/2101.02944 (2021) - [i53]Yihan Du, Yuko Kuroki, Wei Chen:
Combinatorial Pure Exploration with Bottleneck Reward Function and its Extension to General Reward Functions. CoRR abs/2102.12094 (2021) - [i52]Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu:
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning. CoRR abs/2102.12677 (2021) - [i51]Tianyu Pang, Huishuai Zhang, Di He, Yinpeng Dong, Hang Su, Wei Chen, Jun Zhu, Tie-Yan Liu:
Adversarial Training with Rectified Rejection. CoRR abs/2105.14785 (2021) - [i50]Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu:
Machine-Learning Non-Conservative Dynamics for New-Physics Detection. CoRR abs/2106.00026 (2021) - [i49]Shiqi Gong, Qi Meng, Yue Wang, Lijun Wu, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics. CoRR abs/2106.04166 (2021) - [i48]Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, Tie-Yan Liu:
PriorGrad: Improving Conditional Denoising Diffusion Models with Data-Driven Adaptive Prior. CoRR abs/2106.06406 (2021) - [i47]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Large Scale Private Learning via Low-rank Reparametrization. CoRR abs/2106.09352 (2021) - [i46]Xiaobo Liang, Lijun Wu, Juntao Li, Yue Wang, Qi Meng, Tao Qin, Wei Chen, Min Zhang, Tie-Yan Liu:
R-Drop: Regularized Dropout for Neural Networks. CoRR abs/2106.14448 (2021) - [i45]Yichi Zhou, Shihong Song, Huishuai Zhang, Jun Zhu, Wei Chen, Tie-Yan Liu:
Regularized OFU: an Efficient UCB Estimator forNon-linear Contextual Bandit. CoRR abs/2106.15128 (2021) - [i44]Xiangyu Zheng, Xinwei Sun, Wei Chen, Tie-Yan Liu:
Causally Invariant Predictor with Shift-Robustness. CoRR abs/2107.01876 (2021) - [i43]Bohan Wang, Qi Meng, Huishuai Zhang, Ruoyu Sun, Wei Chen, Zhi-Ming Ma:
Momentum Doesn't Change the Implicit Bias. CoRR abs/2110.03891 (2021) - [i42]Bohan Wang, Huishuai Zhang, Jieyu Zhang, Qi Meng, Wei Chen, Tie-Yan Liu:
Optimizing Information-theoretical Generalization Bounds via Anisotropic Noise in SGLD. CoRR abs/2110.13750 (2021) - [i41]Weitao Du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Bin Shao, Tie-Yan Liu:
Equivariant vector field network for many-body system modeling. CoRR abs/2110.14811 (2021) - [i40]Yihan Du, Wei Chen, Yuko Kuroki, Longbo Huang:
Collaborative Pure Exploration in Kernel Bandit. CoRR abs/2110.15771 (2021) - [i39]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Indiscriminate Poisoning Attacks Are Shortcuts. CoRR abs/2111.00898 (2021) - 2020
- [j7]Yue Wang, Yuting Liu, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Target transfer Q-learning and its convergence analysis. Neurocomputing 392: 11-22 (2020) - [j6]Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, Tie-Yan Liu:
Convergence of Distributed Stochastic Variance Reduced Methods Without Sampling Extra Data. IEEE Trans. Signal Process. 68: 3976-3989 (2020) - [c34]Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao:
Combinatorial Pure Exploration for Dueling Bandit. ICML 2020: 1531-1541 - [c33]Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang:
(Locally) Differentially Private Combinatorial Semi-Bandits. ICML 2020: 1757-1767 - [c32]Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang:
Reinforcement Learning with Dynamic Boltzmann Softmax Updates. IJCAI 2020: 1992-1998 - [c31]Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang:
I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations. IJCAI 2020: 2669-2675 - [c30]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Gradient Perturbation is Underrated for Differentially Private Convex Optimization. IJCAI 2020: 3117-3123 - [i38]Wei Chen, Liwei Wang, Haoyu Zhao, Kai Zheng:
Combinatorial Semi-Bandit in the Non-Stationary Environment. CoRR abs/2002.03580 (2020) - [i37]Xiaoyu Chen, Kai Zheng, Zixin Zhou, Yunchang Yang, Wei Chen, Liwei Wang:
(Locally) Differentially Private Combinatorial Semi-Bandits. CoRR abs/2006.00706 (2020) - [i36]Wei Chen, Yihan Du, Yuko Kuroki:
Combinatorial Pure Exploration with Partial or Full-Bandit Linear Feedback. CoRR abs/2006.07905 (2020) - [i35]Wei Chen, Yihan Du, Longbo Huang, Haoyu Zhao:
Combinatorial Pure Exploration of Dueling Bandit. CoRR abs/2006.12772 (2020) - [i34]Qi Meng, Shiqi Gong, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Dynamic of Stochastic Gradient Descent with State-Dependent Noise. CoRR abs/2006.13719 (2020) - [i33]Juanping Zhu, Qi Meng, Wei Chen, Yue Wang, Zhiming Ma:
Constructing Basis Path Set by Eliminating Path Dependency. CoRR abs/2007.00657 (2020) - [i32]Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu:
Membership Inference with Privately Augmented Data Endorses the Benign while Suppresses the Adversary. CoRR abs/2007.10567 (2020) - [i31]Chang Liu, Xinwei Sun, Jindong Wang, Tao Li, Tao Qin, Wei Chen, Tie-Yan Liu:
Learning Causal Semantic Representation for Out-of-Distribution Prediction. CoRR abs/2011.01681 (2020) - [i30]Xinwei Sun, Botong Wu, Chang Liu, Xiangyu Zheng, Wei Chen, Tao Qin, Tie-Yan Liu:
Latent Causal Invariant Model. CoRR abs/2011.02203 (2020) - [i29]Bohan Wang, Qi Meng, Wei Chen:
The Implicit Bias for Adaptive Optimization Algorithms on Homogeneous Neural Networks. CoRR abs/2012.06244 (2020) - [i28]Xinwei Sun, Botong Wu, Wei Chen:
Identifying Invariant Texture Violation for Robust Deepfake Detection. CoRR abs/2012.10580 (2020) - 2019
- [j5]Qi Meng, Wei Chen, Yue Wang, Zhi-Ming Ma, Tie-Yan Liu:
Convergence analysis of distributed stochastic gradient descent with shuffling. Neurocomputing 337: 46-57 (2019) - [j4]Li He, Shuxin Zheng, Wei Chen, Zhiming Ma, Tie-Yan Liu:
OptQuant: Distributed training of neural networks with optimized quantization mechanisms. Neurocomputing 340: 233-244 (2019) - [c29]Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, Tie-Yan Liu:
Capacity Control of ReLU Neural Networks by Basis-Path Norm. AAAI 2019: 5925-5932 - [c28]Qi Meng, Shuxin Zheng, Huishuai Zhang, Wei Chen, Qiwei Ye, Zhi-Ming Ma, Nenghai Yu, Tie-Yan Liu:
G-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space. ICLR (Poster) 2019 - [c27]Mingyang Yi, Huishuai Zhang, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
BN-invariant Sharpness Regularizes the Training Model to Better Generalization. IJCAI 2019: 4164-4170 - [i27]Mingyang Yi, Qi Meng, Wei Chen, Zhi-Ming Ma, Tie-Yan Liu:
Positively Scale-Invariant Flatness of ReLU Neural Networks. CoRR abs/1903.02237 (2019) - [i26]Ling Pan, Qingpeng Cai, Qi Meng, Wei Chen, Longbo Huang, Tie-Yan Liu:
Reinforcement Learning with Dynamic Boltzmann Softmax Updates. CoRR abs/1903.05926 (2019) - [i25]Huishuai Zhang, Da Yu, Wei Chen, Tie-Yan Liu:
Training Over-parameterized Deep ResNet Is almost as Easy as Training a Two-layer Network. CoRR abs/1903.07120 (2019) - [i24]Shicong Cen, Huishuai Zhang, Yuejie Chi, Wei Chen, Tie-Yan Liu:
Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data. CoRR abs/1905.12648 (2019) - [i23]Juanping Zhu, Qi Meng, Wei Chen, Zhiming Ma:
Interpreting Basis Path Set in Neural Networks. CoRR abs/1910.09402 (2019) - [i22]Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu, Jian Yin:
Gradient Perturbation is Underrated for Differentially Private Convex Optimization. CoRR abs/1911.11363 (2019) - 2018
- [j3]Wei Chen, Youzheng Wu, Wenliang Chen, Min Zhang:
基于BiLSTM-CRF的关键词自动抽取 (Automatic Keyword Extraction Based on BiLSTM-CRF). 计算机科学 45(6A): 91-96 (2018) - [c26]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Slim-DP: A Multi-Agent System for Communication-Efficient Distributed Deep Learning. AAMAS 2018: 721-729 - [c25]Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu:
Towards Binary-Valued Gates for Robust LSTM Training. ICML 2018: 3001-3010 - [c24]Li He, Qi Meng, Wei Chen, Zhiming Ma, Tie-Yan Liu:
Differential Equations for Modeling Asynchronous Algorithms. IJCAI 2018: 2220-2226 - [c23]Huishuai Zhang, Wei Chen, Tie-Yan Liu:
On the Local Hessian in Back-propagation. NeurIPS 2018: 6521-6531 - [i21]Qi Meng, Wei Chen, Shuxin Zheng, Qiwei Ye, Tie-Yan Liu:
Optimizing Neural Networks in the Equivalent Class Space. CoRR abs/1802.03713 (2018) - [i20]Huishuai Zhang, Wei Chen, Tie-Yan Liu:
Train Feedfoward Neural Network with Layer-wise Adaptive Rate via Approximating Back-matching Propagation. CoRR abs/1802.09750 (2018) - [i19]Li He, Qi Meng, Wei Chen, Zhiming Ma, Tie-Yan Liu:
Differential Equations for Modeling Asynchronous Algorithms. CoRR abs/1805.02991 (2018) - [i18]Zhuohan Li, Di He, Fei Tian, Wei Chen, Tao Qin, Liwei Wang, Tie-Yan Liu:
Towards Binary-Valued Gates for Robust LSTM Training. CoRR abs/1806.02988 (2018) - [i17]Shuxin Zheng, Qi Meng, Huishuai Zhang, Wei Chen, Nenghai Yu, Tie-Yan Liu:
Capacity Control of ReLU Neural Networks by Basis-path Norm. CoRR abs/1809.07122 (2018) - [i16]Yue Wang, Qi Meng, Wei Chen, Yuting Liu, Zhiming Ma, Tie-Yan Liu:
Target Transfer Q-Learning and Its Convergence Analysis. CoRR abs/1809.08923 (2018) - [i15]Yue Wang, Wei Chen, Yuting Liu, Zhi-Ming Ma, Tie-Yan Liu:
Finite Sample Analysis of the GTD Policy Evaluation Algorithms in Markov Setting. CoRR abs/1809.08926 (2018) - 2017
- [c22]Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhiming Ma, Tie-Yan Liu:
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction. AAAI 2017: 2329-2335 - [c21]Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhiming Ma, Tie-Yan Liu:
Generalization Error Bounds for Optimization Algorithms via Stability. AAAI 2017: 2336-2342 - [c20]Yan Tong, Jian Zhang, Wei Chen, Mingdi Xu, Tao Qin:
Dynamic Group Behavior Analysis and Its Application in Network Abnormal Behavior Detection. ChinaCom (2) 2017: 292-301 - [c19]Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu:
Dual Supervised Learning. ICML 2017: 3789-3798 - [c18]Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu:
Asynchronous Stochastic Gradient Descent with Delay Compensation. ICML 2017: 4120-4129 - [c17]Quanming Yao, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu:
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. IJCAI 2017: 3308-3314 - [c16]Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu:
LightGBM: A Highly Efficient Gradient Boosting Decision Tree. NIPS 2017: 3146-3154 - [c15]Yue Wang, Wei Chen, Yuting Liu, Zhiming Ma, Tie-Yan Liu:
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting. NIPS 2017: 5504-5513 - [c14]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks. ECML/PKDD (1) 2017: 187-202 - [c13]Tie-Yan Liu, Wei Chen, Taifeng Wang:
Distributed Machine Learning: Foundations, Trends, and Practices. WWW (Companion Volume) 2017: 913-915 - [i14]Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu:
Dual Supervised Learning. CoRR abs/1707.00415 (2017) - [i13]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Slim-DP: A Light Communication Data Parallelism for DNN. CoRR abs/1709.09393 (2017) - [i12]Qi Meng, Wei Chen, Yue Wang, Zhiming Ma, Tie-Yan Liu:
Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling. CoRR abs/1709.10432 (2017) - 2016
- [c12]Shizhao Sun, Wei Chen, Liwei Wang, Xiaoguang Liu, Tie-Yan Liu:
On the Depth of Deep Neural Networks: A Theoretical View. AAAI 2016: 2066-2072 - [c11]Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhiming Ma, Tie-Yan Liu:
Asynchronous Accelerated Stochastic Gradient Descent. IJCAI 2016: 1853-1859 - [c10]Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu:
A Communication-Efficient Parallel Algorithm for Decision Tree. NIPS 2016: 1271-1279 - [i11]Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu:
Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks. CoRR abs/1606.00575 (2016) - [i10]Shuxin Zheng, Qi Meng, Taifeng Wang, Wei Chen, Nenghai Yu, Zhiming Ma, Tie-Yan Liu:
Asynchronous Stochastic Gradient Descent with Delay Compensation for Distributed Deep Learning. CoRR abs/1609.08326 (2016) - [i9]Qi Meng, Yue Wang, Wei Chen, Taifeng Wang, Zhiming Ma, Tie-Yan Liu:
Generalization Error Bounds for Optimization Algorithms via Stability. CoRR abs/1609.08397 (2016) - [i8]Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhiming Ma, Tie-Yan Liu:
Asynchronous Stochastic Proximal Optimization Algorithms with Variance Reduction. CoRR abs/1609.08435 (2016) - [i7]Qi Meng, Guolin Ke, Taifeng Wang, Wei Chen, Qiwei Ye, Zhiming Ma, Tie-Yan Liu:
A Communication-Efficient Parallel Algorithm for Decision Tree. CoRR abs/1611.01276 (2016) - 2015
- [c9]Haifang Li, Fei Tian, Wei Chen, Tao Qin, Zhiming Ma, Tie-Yan Liu:
Generalization Analysis for Game-Theoretic Machine Learning. AAAI 2015: 2089-2095 - [c8]Tie-Yan Liu, Wei Chen, Tao Qin:
Mechanism Learning with Mechanism Induced Data. AAAI 2015: 4037-4041 - [i6]Shizhao Sun, Wei Chen, Liwei Wang, Tie-Yan Liu:
Large Margin Deep Neural Networks: Theory and Algorithms. CoRR abs/1506.05232 (2015) - 2014
- [j2]Tao Qin, Wei Chen, Tie-Yan Liu:
Sponsored Search Auctions: Recent Advances and Future Directions. ACM Trans. Intell. Syst. Technol. 5(4): 60:1-60:34 (2014) - [c7]Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu:
Agent Behavior Prediction and Its Generalization Analysis. AAAI 2014: 1300-1306 - [c6]Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang:
Generalized second price auction with probabilistic broad match. EC 2014: 39-56 - [c5]Jun Feng, Jiang Bian, Taifeng Wang, Wei Chen, Xiaoyan Zhu, Tie-Yan Liu:
Sampling dilemma: towards effective data sampling for click prediction in sponsored search. WSDM 2014: 103-112 - [i5]Wei Chen, Di He, Tie-Yan Liu, Tao Qin, Yixin Tao, Liwei Wang:
Generalized Second Price Auction with Probabilistic Broad Match. CoRR abs/1404.3828 (2014) - [i4]Fei Tian, Haifang Li, Wei Chen, Tao Qin, Enhong Chen, Tie-Yan Liu:
Agent Behavior Prediction and Its Generalization Analysis. CoRR abs/1404.4960 (2014) - [i3]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search. CoRR abs/1406.0728 (2014) - [i2]Haifang Li, Fei Tian, Wei Chen, Tao Qin, Tie-Yan Liu:
Generalization Analysis for Game-Theoretic Machine Learning. CoRR abs/1410.3341 (2014) - 2013
- [j1]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
Online learning for auction mechanism in bandit setting. Decis. Support Syst. 56: 379-386 (2013) - [c4]Di He, Wei Chen, Liwei Wang, Tie-Yan Liu:
A Game-Theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search. IJCAI 2013: 206-212 - [i1]Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Tie-Yan Liu, Wei Chen:
A Theoretical Analysis of NDCG Type Ranking Measures. CoRR abs/1304.6480 (2013) - 2012
- [c3]Lei Yao, Wei Chen, Tie-Yan Liu:
Convergence Analysis for Weighted Joint Strategy Fictitious Play in Generalized Second Price Auction. WINE 2012: 489-495 - 2010
- [c2]Wei Chen, Tie-Yan Liu, Zhiming Ma:
Two-Layer Generalization Analysis for Ranking Using Rademacher Average. NIPS 2010: 370-378 - 2009
- [c1]Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li:
Ranking Measures and Loss Functions in Learning to Rank. NIPS 2009: 315-323
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