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ORCIDQiang Liu 0001
Person information
- affiliation: University of Texas at Austin, Department of Computer Science, TX, USA
- affiliation (former): Dartmouth College, Department of Computer Science, Hanover, NH, USA
- affiliation (former): University of California, Irvine, Department of Computer Science, CA, USA
Other persons with the same name
- Qiang Liu — disambiguation page
- Qiang Liu 0002 — Linköping University, Swedish e-Science Research Centre, Sweden
- Qiang Liu 0003
![[0000-0001-7531-4459]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0001-7531-4459")
— University of Essex, School of Computer Science and Electronic Engineering, Colchester, UK - Qiang Liu 0004 — National University of Defense Technology, College of Computer, Changsha, China
- Qiang Liu 0005 — Delft University of Technology, The Netherlands (and 1 more)
- Qiang Liu 0006 — Chinese Academy of Sciences, Institute of Automation, Center for Research on Intelligent Perception and Computing, Beijing, China
- Qiang Liu 0007 — Oak Ridge National Laboratory, Computer Science and Mathematics Division, TN, USA (and 1 more)
- Qiang Liu 0008 — Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China (and 1 more)
- Qiang Liu 0009 — Joint Center for Global Change Studies, Beijing, China (and 2 more)
- Qiang Liu 0010 — Liaoning Shihua University, School of Information and Control Engineering, Fushun, China (and 1 more)
- Qiang Liu 0011 — Tianjin University, School of Microelectronics, China (and 1 more)
- Qiang Liu 0012 — Huaihai Institute of Technology, School of Electric Engineering, Lianyungang, China
- Qiang Liu 0013 — University of Nebraska-Lincoln, NE, USA (and 1 more)
- Qiang Liu 0014 — Beijing Jiaotong University, School of Computer and Information Technology, China
- Qiang Liu 0015 — Chinese University of Hong Kong
- Qiang Liu 0016 — University of Electronic Science and Technology of China, School of Communication and Information Engineering, Chengdu, China
- Qiang Liu 0017 — Hunan University, College of Electrical and Information Engineering, Changsha, China (and 1 more)
- Qiang Liu 0018 — Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Shenyang, China
- Qiang Liu 0019 — University of Manchester, School of Engineering, UK
- Qiang Liu 0020 — Beijing Jiaotong University, Beijing Key Laboratory of Transportation Data Analysis and Mining, China
- Qiang Liu 0021 — Sichuan University, College of Electronics and Information Engineering, Chengdu, China
- Qiang Liu 0022 — University of Antwerp, Department of Biology, Belgium (and 1 more)
- Qiang Liu 0023 — Dalian University of Technology, School of Mechanical Engineering, China
- Qiang Liu 0024 — Beihang University, School of Mechanical Engineering and Automation, Beijing, China
- Qiang Liu 0025 — Shenzhen University, School of Mathematics and Statistics, China (and 1 more)
- Qiang Liu 0026 — Shandong University of Finance and Economics, Department of Network and Information Security, Jinan, China (and 1 more)
- Qiang Liu 0027 — Chinese Academy of Sciences, Changchun Institute of Optics, Fine Mechanics and Physics, China
- Qiang Liu 0028 — Harbin Institute of Technology, State Key Laboratory of Robotics and System, China
- Qiang Liu 0029 — Beijing Institute of Petrochemical Technology, Institute of Precision Electromagnetic Equipment and Advanced Measurement Technology, China (and 1 more)
- Qiang Liu 0030 — Beijing University of Posts and Telecommunications, School of Information and Communication, China
- Qiang Liu 0031 — Guangdong University of Technology, Guangzhou, China
- Qiang Liu 0032 — Hunan University of Technology, College of Computer Science, Intelligent Information Perception and Processing Technology Hunan Province Key Laboratory, Zhuzhou, China
- Qiang Liu 0033 — University of Pittsburgh, Department of Neurological Surgery, PA, USA
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2020 – today
- 2024
[c100]Chengyue Gong, Xiaocong Du, Bhargav Bhushanam, Lemeng Wu, Xingchao Liu, Dhruv Choudhary, Arun Kejariwal, Qiang Liu:
Layer Compression of Deep Networks with Straight Flows. AAAI 2024: 12181-12189- [i91]Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra:
SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity. CoRR abs/2401.00604 (2024) - 2023
- [c99]Bo Liu, Yihao Feng, Qiang Liu, Peter Stone:
Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning. AAAI 2023: 8799-8806 - [c98]Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu:
Fast Point Cloud Generation with Straight Flows. CVPR 2023: 9445-9454 - [c97]Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu:
FlowGrad: Controlling the Output of Generative ODEs with Gradients. CVPR 2023: 24335-24344 - [c96]Xin Sun, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang:
Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction. EMNLP (Findings) 2023: 13145-13157 - [c95]Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu:
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. ICCV 2023: 8404-8416 - [c94]Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew D. Ellington, Alex Dimakis, Adam R. Klivans:
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. ICLR 2023 - [c93]Xingchao Liu, Chengyue Gong, Qiang Liu:
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. ICLR 2023 - [c92]Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu:
Learning Diffusion Bridges on Constrained Domains. ICLR 2023 - [c91]Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu:
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. ICML 2023: 11827-11846 - [c90]Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma:
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. ICML 2023: 27611-27629 - [i90]Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu:
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. CoRR abs/2303.05078 (2023) - [i89]Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma:
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. CoRR abs/2305.07508 (2023) - [i88]Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone:
LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning. CoRR abs/2306.03310 (2023) - [i87]Bo Liu, Yihao Feng, Peter Stone, Qiang Liu:
FAMO: Fast Adaptive Multitask Optimization. CoRR abs/2306.03792 (2023) - [i86]Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu:
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation. CoRR abs/2309.06380 (2023) - [i85]Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang:
Uncovering Neural Scaling Laws in Molecular Representation Learning. CoRR abs/2309.15123 (2023) - 2022
- [j9]Di Wu, Zhanxiu Zeng, Fengrui Shi, Weiren Yu, Tao Wu, Qiang Liu:
Human as a Service: Towards Resilient Parking Search System With Sensorless Sensing. IEEE Trans. Intell. Transp. Syst. 23(8): 13863-13877 (2022) - [c89]Bo Liu, Qiang Liu, Peter Stone:
Continual Learning and Private Unlearning. CoLLAs 2022: 243-254 - [c88]Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra:
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training. ICLR 2022 - [c87]Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng:
Energy-Inspired Molecular Conformation Optimization. ICLR 2022 - [c86]Chengyue Gong, Lemeng Wu, Qiang Liu:
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. ICML 2022: 7650-7664 - [c85]Mao Ye, Qiang Liu:
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML 2022: 25469-25489 - [c84]Ruqi Zhang, Xingchao Liu, Qiang Liu:
A Langevin-like Sampler for Discrete Distributions. ICML 2022: 26375-26396 - [c83]Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal:
Harmless Transfer Learning for Item Embeddings. NAACL-HLT (Findings) 2022: 504-516 - [c82]Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS 2022 - [c81]Mao Ye, Lemeng Wu, Qiang Liu:
First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data. NeurIPS 2022 - [c80]Ruqi Zhang, Qiang Liu, Xin T. Tong:
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. NeurIPS 2022 - [c79]Mao Ye, Qiang Liu:
Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. UAI 2022: 2246-2255 - [c78]Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu:
Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. UAI 2022: 2256-2266 - [i84]Ziyang Tang, Yihao Feng, Qiang Liu:
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning. CoRR abs/2201.00236 (2022) - [i83]Chengyue Gong, Lemeng Wu, Qiang Liu:
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. CoRR abs/2202.08376 (2022) - [i82]Bo Liu, Qiang Liu, Peter Stone:
Continual Learning and Private Unlearning. CoRR abs/2203.12817 (2022) - [i81]Ruqi Zhang, Xingchao Liu, Qiang Liu:
A Langevin-like Sampler for Discrete Distributions. CoRR abs/2206.09914 (2022) - [i80]Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu:
Split Localized Conformal Prediction. CoRR abs/2206.13092 (2022) - [i79]Bo Liu, Yihao Feng, Qiang Liu, Peter Stone:
Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning. CoRR abs/2208.08133 (2022) - [i78]Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu:
Let us Build Bridges: Understanding and Extending Diffusion Generative Models. CoRR abs/2208.14699 (2022) - [i77]Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. CoRR abs/2209.00865 (2022) - [i76]Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu:
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems. CoRR abs/2209.01143 (2022) - [i75]Mao Ye, Lemeng Wu, Qiang Liu:
First Hitting Diffusion Models. CoRR abs/2209.01170 (2022) - [i74]Xingchao Liu, Chengyue Gong, Qiang Liu:
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. CoRR abs/2209.03003 (2022) - [i73]Mao Ye, Bo Liu, Stephen Wright, Peter Stone, Qiang Liu:
BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. CoRR abs/2209.08709 (2022) - [i72]Yan Zheng, Lemeng Wu, Xingchao Liu, Zhen Chen, Qiang Liu, Qixing Huang:
Neural Volumetric Mesh Generator. CoRR abs/2210.03158 (2022) - [i71]Ruqi Zhang, Qiang Liu, Xin T. Tong:
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. CoRR abs/2210.06447 (2022) - [i70]Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu:
Fast Point Cloud Generation with Straight Flows. CoRR abs/2212.01747 (2022) - [i69]Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra:
PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion. CoRR abs/2212.06244 (2022) - 2021
- [j8]Aishan Liu, Xianglong Liu, Hang Yu, Chongzhi Zhang, Qiang Liu, Dacheng Tao:
Training Robust Deep Neural Networks via Adversarial Noise Propagation. IEEE Trans. Image Process. 30: 5769-5781 (2021) - [c77]Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu:
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. AAAI 2021: 8697-8705 - [c76]Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu:
KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CVPR 2021: 1055-1064 - [c75]Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu:
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training. CVPR 2021: 2474-2483 - [c74]Chengyue Gong, Dilin Wang, Qiang Liu:
AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. CVPR 2021: 13683-13692 - [c73]Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu:
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. ICLR 2021 - [c72]Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae:
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. ICLR 2021 - [c71]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar:
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML 2021: 6860-6870 - [c70]Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra:
AlphaNet: Improved Training of Supernets with Alpha-Divergence. ICML 2021: 10760-10771 - [c69]Chengyue Gong, Mao Ye, Qiang Liu:
argmax centroid. NeurIPS 2021: 7012-7024 - [c68]Xingchao Liu, Xin Tong, Qiang Liu:
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent. NeurIPS 2021: 14721-14733 - [c67]Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu:
Conflict-Averse Gradient Descent for Multi-task learning. NeurIPS 2021: 18878-18890 - [c66]Xingchao Liu, Xin Tong, Qiang Liu:
Sampling with Trusthworthy Constraints: A Variational Gradient Framework. NeurIPS 2021: 23557-23568 - [c65]Chengyue Gong, Xingchao Liu, Qiang Liu:
Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach. NeurIPS 2021: 29630-29642 - [i68]Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra:
AlphaNet: Improved Training of Supernet with Alpha-Divergence. CoRR abs/2102.07954 (2021) - [i67]Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu:
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. CoRR abs/2102.08574 (2021) - [i66]Lemeng Wu, Xingchao Liu, Qiang Liu:
Centroid Transformers: Learning to Abstract with Attention. CoRR abs/2102.08606 (2021) - [i65]Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu:
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. CoRR abs/2103.05741 (2021) - [i64]Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae:
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. CoRR abs/2103.07861 (2021) - [i63]Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu:
Improve Vision Transformers Training by Suppressing Over-smoothing. CoRR abs/2104.12753 (2021) - [i62]Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar:
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR abs/2105.08692 (2021) - [i61]Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou:
Speeding up Deep Model Training by Sharing Weights and Then Unsharing. CoRR abs/2110.03848 (2021) - [i60]Mao Ye, Qiang Liu:
Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set. CoRR abs/2110.08713 (2021) - [i59]Mao Ye, Qiang Liu:
Centroid Approximation for Bootstrap. CoRR abs/2110.08720 (2021) - [i58]Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu:
Conflict-Averse Gradient Descent for Multi-task Learning. CoRR abs/2110.14048 (2021) - [i57]Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu:
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization. CoRR abs/2112.01573 (2021) - 2020
- [j7]Qiang Liu, Xin T. Tong:
Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations. Stat. Comput. 30(4): 1037-1056 (2020) - [j6]Di Wu, Lambros Lambrinos, Thomas Przepiorka, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann:
Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems. IEEE Trans. Intell. Transp. Syst. 21(7): 2750-2764 (2020) - [c64]Mao Ye, Chengyue Gong, Qiang Liu:
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. ACL 2020: 3465-3475 - [c63]Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu:
Stein Variational Inference for Discrete Distributions. AISTATS 2020: 4563-4572 - [c62]Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou:
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. ICLR 2020 - [c61]Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu:
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. ICLR 2020 - [c60]Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu:
Accountable Off-Policy Evaluation With Kernel Bellman Statistics. ICML 2020: 3102-3111 - [c59]Xianggen Liu, Qiang Liu, Sen Song, Jian Peng:
A Chance-Constrained Generative Framework for Sequence Optimization. ICML 2020: 6271-6281 - [c58]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML 2020: 10820-10830 - [c57]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c56]Xingchao Liu, Xing Han, Na Zhang, Qiang Liu:
Certified Monotonic Neural Networks. NeurIPS 2020 - [c55]Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu:
Off-Policy Interval Estimation with Lipschitz Value Iteration. NeurIPS 2020 - [c54]Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization and Convergence for Weight Normalization. NeurIPS 2020 - [c53]Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu:
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. NeurIPS 2020 - [c52]Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. NeurIPS 2020 - [c51]Mao Ye, Lemeng Wu, Qiang Liu:
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. NeurIPS 2020 - [c50]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS 2020 - [i56]ChengYue Gong, Tongzheng Ren, Mao Ye, Qiang Liu:
MaxUp: A Simple Way to Improve Generalization of Neural Network Training. CoRR abs/2002.09024 (2020) - [i55]Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu:
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. CoRR abs/2002.09049 (2020) - [i54]Mao Ye, Tongzheng Ren, Qiang Liu:
Stein Self-Repulsive Dynamics: Benefits From Past Samples. CoRR abs/2002.09070 (2020) - [i53]Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu:
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR abs/2002.09169 (2020) - [i52]Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao:
Statistical Adaptive Stochastic Gradient Methods. CoRR abs/2002.10597 (2020) - [i51]Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu:
Stein Variational Inference for Discrete Distributions. CoRR abs/2003.00605 (2020) - [i50]Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu:
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR abs/2003.01794 (2020) - [i49]Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu:
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. CoRR abs/2003.10392 (2020) - [i48]Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou:
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. CoRR abs/2003.11126 (2020) - [i47]Xi Chen, Qiang Liu, Xin T. Tong:
Dimension Independent Generalization Error with Regularized Online Optimization. CoRR abs/2003.11196 (2020) - [i46]Mao Ye, Chengyue Gong, Qiang Liu:
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. CoRR abs/2005.14424 (2020) - [i45]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i44]Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu:
Accountable Off-Policy Evaluation With Kernel Bellman Statistics. CoRR abs/2008.06668 (2020) - [i43]Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal:
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data. CoRR abs/2010.08655 (2020) - [i42]Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu:
Off-Policy Interval Estimation with Lipschitz Value Iteration. CoRR abs/2010.15392 (2020) - [i41]Mao Ye, Lemeng Wu, Qiang Liu:
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. CoRR abs/2010.15969 (2020) - [i40]Xingchao Liu, Xing Han, Na Zhang, Qiang Liu:
Certified Monotonic Neural Networks. CoRR abs/2011.10219 (2020) - [i39]Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu:
KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CoRR abs/2011.11778 (2020) - [i38]Chengyue Gong, Dilin Wang, Qiang Liu:
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence. CoRR abs/2011.11779 (2020)
2010 – 2019
- 2019
- [j5]Zan Liu, Xihong Chen, Qiang Liu:
Estimating Zenith Tropospheric Delay Based on GPT2w Model. IEEE Access 7: 139258-139263 (2019) - [j4]Fengrui Shi, Di Wu, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann:
ParkCrowd: Reliable Crowdsensing for Aggregation and Dissemination of Parking Space Information. IEEE Trans. Intell. Transp. Syst. 20(11): 4032-4044 (2019) - [c49]Wei Ye, Yibo Lin, Meng Li, Qiang Liu, David Z. Pan:
LithoROC: lithography hotspot detection with explicit ROC optimization. ASP-DAC 2019: 292-298 - [c48]ChengYue Gong, Zixuan Jiang, Dilin Wang, Yibo Lin, Qiang Liu, David Z. Pan:
Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning. ICCAD 2019: 1-7 - [c47]Tanmay Gangwani, Qiang Liu, Jian Peng:
Learning Self-Imitating Diverse Policies. ICLR (Poster) 2019 - [c46]Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng:
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. ICLR (Poster) 2019 - [c45]ChengYue Gong, Jian Peng, Qiang Liu:
Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization. ICML 2019: 2347-2356 - [c44]Dilin Wang, ChengYue Gong, Qiang Liu:
Improving Neural Language Modeling via Adversarial Training. ICML 2019: 6555-6565 - [c43]Dilin Wang, Qiang Liu:
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. ICML 2019: 6576-6585 - [c42]Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu:
Stein Variational Gradient Descent With Matrix-Valued Kernels. NeurIPS 2019: 7834-7844 - [c41]Lemeng Wu, Dilin Wang, Qiang Liu:
Splitting Steepest Descent for Growing Neural Architectures. NeurIPS 2019: 10655-10665 - [c40]Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng:
Exploration via Hindsight Goal Generation. NeurIPS 2019: 13464-13474 - [c39]Yihao Feng, Lihong Li, Qiang Liu:
A Kernel Loss for Solving the Bellman Equation. NeurIPS 2019: 15430-15441 - [c38]Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng:
Learning Belief Representations for Imitation Learning in POMDPs. UAI 2019: 1061-1071 - [i37]Yihao Feng, Lihong Li, Qiang Liu:
A Kernel Loss for Solving the Bellman Equation. CoRR abs/1905.10506 (2019) - [i36]Yang Liu, Yunan Luo, Yuanyi Zhong, Xi Chen, Qiang Liu, Jian Peng:
Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning. CoRR abs/1905.13420 (2019) - [i35]Dilin Wang, ChengYue Gong, Qiang Liu:
Improving Neural Language Modeling via Adversarial Training. CoRR abs/1906.03805 (2019) - [i34]Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng:
Exploration via Hindsight Goal Generation. CoRR abs/1906.04279 (2019) - [i33]Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng:
Learning Belief Representations for Imitation Learning in POMDPs. CoRR abs/1906.09510 (2019) - [i32]Aishan Liu, Xianglong Liu, Chongzhi Zhang, Hang Yu, Qiang Liu, Junfeng He:
Training Robust Deep Neural Networks via Adversarial Noise Propagation. CoRR abs/1909.09034 (2019) - [i31]Qiang Liu, Lemeng Wu, Dilin Wang:
Splitting Steepest Descent for Growing Neural Architectures. CoRR abs/1910.02366 (2019) - [i30]Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu:
Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent. CoRR abs/1910.03103 (2019) - [i29]Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu:
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. CoRR abs/1910.07186 (2019) - [i28]Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu:
Stein Variational Gradient Descent With Matrix-Valued Kernels. CoRR abs/1910.12794 (2019) - [i27]Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu:
Implicit Regularization of Normalization Methods. CoRR abs/1911.07956 (2019) - 2018
- [c37]Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu:
Action-dependent Control Variates for Policy Optimization via Stein Identity. ICLR (Poster) 2018 - [c36]Dilin Wang, Qiang Liu:
An Optimization View on Dynamic Routing Between Capsules. ICLR (Workshop) 2018 - [c35]Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He:
On the Discrimination-Generalization Tradeoff in GANs. ICLR (Poster) 2018 - [c34]Jun Han, Qiang Liu:
Stein Variational Gradient Descent Without Gradient. ICML 2018: 1895-1903 - [c33]Dilin Wang, Zhe Zeng, Qiang Liu:
Stein Variational Message Passing for Continuous Graphical Models. ICML 2018: 5206-5214 - [c32]Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng:
Learning to Explore via Meta-Policy Gradient. ICML 2018: 5459-5468 - [c31]Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng:
Energy-efficient Amortized Inference with Cascaded Deep Classifiers. IJCAI 2018: 2184-2190 - [c30]Jinglin Chen, Jian Peng, Qiang Liu:
Efficient Localized Inference for Large Graphical Models. IJCAI 2018: 4987-4993 - [c29]Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou:
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. NeurIPS 2018: 5361-5371 - [c28]Dilin Wang, Hao Liu, Qiang Liu:
Variational Inference with Tail-adaptive f-Divergence. NeurIPS 2018: 5742-5752 - [c27]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent as Moment Matching. NeurIPS 2018: 8868-8877 - [i26]Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng:
Learning to Explore with Meta-Policy Gradient. CoRR abs/1803.05044 (2018) - [i25]Tanmay Gangwani, Qiang Liu, Jian Peng:
Learning Self-Imitating Diverse Policies. CoRR abs/1805.10309 (2018) - [i24]Jun Han, Qiang Liu:
Stein Variational Gradient Descent Without Gradient. CoRR abs/1806.02775 (2018) - [i23]Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng:
Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. CoRR abs/1808.00232 (2018) - [i22]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent as Moment Matching. CoRR abs/1810.11693 (2018) - [i21]Dilin Wang, Hao Liu, Qiang Liu:
Variational Inference with Tail-adaptive f-Divergence. CoRR abs/1810.11943 (2018) - [i20]Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou:
Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. CoRR abs/1810.12429 (2018) - 2017
- [j3]Di Wu, Dmitri I. Arkhipov, Thomas Przepiorka, Yong Li, Bin Guo, Qiang Liu:
From Intermittent to Ubiquitous: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1(3): 114:1-114:32 (2017) - [c26]Yihao Feng, Dilin Wang, Qiang Liu:
Learning to Draw Samples with Amortized Stein Variational Gradient Descent. UAI 2017 - [c25]Jun Han, Qiang Liu:
Stein Variational Adaptive Importance Sampling. UAI 2017 - [c24]Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng:
Stein Variational Policy Gradient. UAI 2017 - [i19]Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng:
Stein Variational Policy Gradient. CoRR abs/1704.02399 (2017) - [i18]Qiang Liu, Dilin Wang:
Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE. CoRR abs/1707.00797 (2017) - [i17]Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng:
Energy-efficient Amortized Inference with Cascaded Deep Classifiers. CoRR abs/1710.03368 (2017) - [i16]Wei Ping, Qiang Liu, Alexander Ihler:
Learning Infinite RBMs with Frank-Wolfe. CoRR abs/1710.05270 (2017) - [i15]Tianbing Xu, Qiang Liu, Jian Peng:
Stochastic Variance Reduction for Policy Gradient Estimation. CoRR abs/1710.06034 (2017) - [i14]Jinglin Chen, Jian Peng, Qiang Liu:
Efficient Localized Inference for Large Graphical Models. CoRR abs/1710.10404 (2017) - [i13]Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu:
Sample-efficient Policy Optimization with Stein Control Variate. CoRR abs/1710.11198 (2017) - [i12]Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He:
On the Discrimination-Generalization Tradeoff in GANs. CoRR abs/1711.02771 (2017) - [i11]Dilin Wang, Zhe Zeng, Qiang Liu:
Structured Stein Variational Inference for Continuous Graphical Models. CoRR abs/1711.07168 (2017) - 2016
- [c23]Jun Han, Qiang Liu:
Bootstrap Model Aggregation for Distributed Statistical Learning. NIPS 2016: 1795-1803 - [c22]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. NIPS 2016: 2370-2378 - [c21]Wei Ping, Qiang Liu, Alexander Ihler:
Learning Infinite RBMs with Frank-Wolfe. NIPS 2016: 3063-3071 - [c20]Dilin Wang, John W. Fisher III, Qiang Liu:
Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds. UAI 2016 - [i10]Jun Han, Qiang Liu:
Bootstrap Model Aggregation for Distributed Statistical Learning. CoRR abs/1607.01036 (2016) - [i9]Qiang Liu, Dilin Wang:
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. CoRR abs/1608.04471 (2016) - [i8]Dilin Wang, Qiang Liu:
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. CoRR abs/1611.01722 (2016) - 2015
- [c19]Qiang Liu, Alexander Ihler, John W. Fisher III:
Boosting crowdsourcing with expert labels: Local vs. global effects. FUSION 2015: 9-14 - [c18]Qiang Liu, John W. Fisher III, Alexander Ihler:
Probabilistic Variational Bounds for Graphical Models. NIPS 2015: 1432-1440 - [c17]Wei Ping, Qiang Liu, Alexander Ihler:
Decomposition Bounds for Marginal MAP. NIPS 2015: 3267-3275 - [c16]Qiang Liu, Jian Peng, Alexander Ihler, John W. Fisher III:
Estimating the Partition Function by Discriminance Sampling. UAI 2015: 514-522 - [i7]Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah:
Regularized Minimax Conditional Entropy for Crowdsourcing. CoRR abs/1503.07240 (2015) - [i6]Wei Ping, Qiang Liu, Alexander Ihler:
Decomposition Bounds for Marginal MAP. CoRR abs/1511.02619 (2015) - 2014
- [b1]Qiang Liu:
Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. University of California, Irvine, USA, 2014 - [c15]Wei Ping, Qiang Liu, Alexander Ihler:
Marginal Structured SVM with Hidden Variables. ICML 2014: 190-198 - [c14]Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek:
Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. ICML 2014: 262-270 - [c13]Qiang Liu, Alexander Ihler:
Distributed Estimation, Information Loss and Exponential Families. NIPS 2014: 1098-1106 - [i5]Wei Ping, Qiang Liu, Alexander Ihler:
Marginal Structured SVM with Hidden Variables. CoRR abs/1409.1320 (2014) - 2013
- [j2]Qiang Liu, Alexander Ihler:
Variational algorithms for marginal MAP. J. Mach. Learn. Res. 14(1): 3165-3200 (2013) - [c12]Qiang Liu, Alexander Ihler, Mark Steyvers:
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? NIPS 2013: 1914-1922 - [c11]Qiang Cheng, Qiang Liu, Feng Chen, Alexander Ihler:
Variational Planning for Graph-based MDPs. NIPS 2013: 2976-2984 - [i4]Qiang Liu, Alexander Ihler:
Variational Algorithms for Marginal MAP. CoRR abs/1302.6584 (2013) - 2012
- [c10]Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J. C. Burges, Ainur Yessenalina, Qiang Liu:
Computational Approaches to Sentence Completion. ACL (1) 2012: 601-610 - [c9]Qiang Liu, Alexander Ihler:
Distributed Parameter Estimation via Pseudo-likelihood . ICML 2012 - [c8]Qiang Liu, Jian Peng, Alexander Ihler:
Variational Inference for Crowdsourcing. NIPS 2012: 701-709 - [c7]Qiang Liu, Alexander Ihler:
Belief Propagation for Structured Decision Making. UAI 2012: 523-532 - [i3]Qiang Liu, Alexander Ihler:
Variational Algorithms for Marginal MAP. CoRR abs/1202.3742 (2012) - [i2]Qiang Liu, Alexander Ihler:
Negative Tree Reweighted Belief Propagation. CoRR abs/1203.3494 (2012) - [i1]Qiang Liu, Alexander Ihler:
Belief Propagation for Structured Decision Making. CoRR abs/1210.4897 (2012) - 2011
- [c6]Qiang Liu, Alexander Ihler:
Bounding the Partition Function using Holder's Inequality. ICML 2011: 849-856 - [c5]Qiang Liu, Alexander Ihler:
Variational Algorithms for Marginal MAP. UAI 2011: 453-462 - [c4]Qiang Liu, Alexander Ihler:
Learning Scale Free Networks by Reweighted L1 regularization. AISTATS 2011: 40-48 - 2010
- [j1]Qiang Liu, Kevin K. Lin, Bogi Andersen, Padhraic Smyth, Alexander Ihler:
Estimating replicate time shifts using Gaussian process regression. Bioinform. 26(6): 770-776 (2010) - [c3]Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth:
Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. ICML 2010: 47-54 - [c2]Qiang Liu, Alexander Ihler:
Negative Tree Reweighted Belief Propagation. UAI 2010: 332-339 - [c1]Arthur U. Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth:
Learning with Blocks: Composite Likelihood and Contrastive Divergence. AISTATS 2010: 33-40
Coauthor Index
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