


Остановите войну!
for scientists:


default search action
Qiang 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
— 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
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
- [i86]Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu:
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. CoRR abs/2303.05078 (2023) - [i85]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) - 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) - [c88]Bo Liu, Qiang Liu, Peter Stone:
Continual Learning and Private Unlearning. CoLLAs 2022: 243-254 - [c87]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 - [c86]Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng:
Energy-Inspired Molecular Conformation Optimization. ICLR 2022 - [c85]Chengyue Gong, Lemeng Wu, Qiang Liu:
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. ICML 2022: 7650-7664 - [c84]Mao Ye, Qiang Liu:
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML 2022: 25469-25489 - [c83]Ruqi Zhang, Xingchao Liu, Qiang Liu:
A Langevin-like Sampler for Discrete Distributions. ICML 2022: 26375-26396 - [c82]Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal:
Harmless Transfer Learning for Item Embeddings. NAACL-HLT (Findings) 2022: 504-516 - [c81]Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu:
Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS 2022 - [c80]Ruqi Zhang, Qiang Liu, Xin 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 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 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]