Qiang Liu

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 158

Top Venues

CoRR

NeurIPS

ICML

ICLR

University of California, Irvine, USA (PHD)

Education History

University of California, Irvine, USA

PHD

Publications

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 (2024)
Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. CoRR (2023)
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu
GSLB: The Graph Structure Learning Benchmark. CoRR (2023)
Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu
FlowGrad: Controlling the Output of Generative ODEs with Gradients. CVPR (2023)
Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. ICCV (2023)
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)
Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu
Learning Diffusion Bridges on Constrained Domains. ICLR (2023)
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 (2023)
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)
Bo Liu, Yihao Feng, Qiang Liu, Peter Stone
Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning. AAAI (2023)
Xin Sun, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang
Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction. EMNLP (Findings) (2023)
Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. CoRR (2023)
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 (2023)
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)
Xingchao Liu, Chengyue Gong, Qiang Liu
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. ICLR (2023)
Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. ICML (2023)
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 (2022)
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu
Diffusion-based Molecule Generation with Informative Prior Bridges. CoRR (2022)
Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
Energy-Inspired Molecular Conformation Optimization. ICLR (2022)
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)
Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu
Split Localized Conformal Prediction. CoRR (2022)
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu
Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS (2022)
Mao Ye, Qiang Liu
Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. UAI (2022)
Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu
Let us Build Bridges: Understanding and Extending Diffusion Generative Models. CoRR (2022)
Ziyang Tang, Yihao Feng, Qiang Liu
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning. CoRR (2022)
Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal
Harmless Transfer Learning for Item Embeddings. NAACL-HLT (Findings) (2022)
Ruqi Zhang, Qiang Liu, Xin T. Tong
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. CoRR (2022)
Ruqi Zhang, Xingchao Liu, Qiang Liu
A Langevin-like Sampler for Discrete Distributions. CoRR (2022)
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 (2022)
Bo Liu, Qiang Liu, Peter Stone
Continual Learning and Private Unlearning. CoRR (2022)
Mao Ye, Qiang Liu
Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML (2022)
Mao Ye, Lemeng Wu, Qiang Liu
First Hitting Diffusion Models. CoRR (2022)
Chengyue Gong, Lemeng Wu, Qiang Liu
How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. ICML (2022)
Ruqi Zhang, Xingchao Liu, Qiang Liu
A Langevin-like Sampler for Discrete Distributions. ICML (2022)
Ruqi Zhang, Qiang Liu, Xin Tong
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. NeurIPS (2022)
Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra
PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion. CoRR (2022)
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 (2021)
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CVPR (2021)
Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. ICLR (2021)
Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
AlphaNet: Improved Training of Supernets with Alpha-Divergence. ICML (2021)
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
Conflict-Averse Gradient Descent for Multi-task Learning. CoRR (2021)
Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR (2021)
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
Improve Vision Transformers Training by Suppressing Over-smoothing. CoRR (2021)
Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. CoRR (2021)
Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. CoRR (2021)
Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu
MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training. CVPR (2021)
Xingchao Liu, Xin Tong, Qiang Liu
Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent. NeurIPS (2021)
Chengyue Gong, Xingchao Liu, Qiang Liu
Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach. NeurIPS (2021)
Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
AlphaNet: Improved Training of Supernet with Alpha-Divergence. CoRR (2021)
Chengyue Gong, Mao Ye, Qiang Liu
argmax centroid. NeurIPS (2021)
Mao Ye, Qiang Liu
Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set. CoRR (2021)
Mao Ye, Qiang Liu
Centroid Approximation for Bootstrap. CoRR (2021)
Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. ICLR (2021)
Chengyue Gong, Dilin Wang, Qiang Liu
AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. CVPR (2021)
Xingchao Liu, Xin Tong, Qiang Liu
Sampling with Trusthworthy Constraints: A Variational Gradient Framework. NeurIPS (2021)
Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar
Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML (2021)
Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. CoRR (2021)
Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. AAAI (2021)
Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
Conflict-Averse Gradient Descent for Multi-task learning. NeurIPS (2021)
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 (2021)
Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou
Speeding up Deep Model Training by Sharing Weights and Then Unsharing. CoRR (2021)
Mao Ye, Chengyue Gong, Qiang Liu
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. ACL (2020)
Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu
Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. NeurIPS (2020)
Chengyue Gong, Dilin Wang, Qiang Liu
AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence. CoRR (2020)
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)
Mao Ye, Lemeng Wu, Qiang Liu
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. CoRR (2020)
Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
Off-Policy Interval Estimation with Lipschitz Value Iteration. NeurIPS (2020)
Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
Accountable Off-Policy Evaluation With Kernel Bellman Statistics. ICML (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR (2020)
Mao Ye, Tongzheng Ren, Qiang Liu
Stein Self-Repulsive Dynamics: Benefits From Past Samples. NeurIPS (2020)
Mao Ye, Chengyue Gong, Qiang Liu
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. CoRR (2020)
Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu
Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. CoRR (2020)
Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. ICLR (2020)
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR (2020)
Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS (2020)
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)
Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML (2020)
Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
Certified Monotonic Neural Networks. NeurIPS (2020)
ChengYue Gong, Tongzheng Ren, Mao Ye, Qiang Liu
MaxUp: A Simple Way to Improve Generalization of Neural Network Training. CoRR (2020)
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 (2020)
Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
Accountable Off-Policy Evaluation With Kernel Bellman Statistics. CoRR (2020)
Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao
Statistical Adaptive Stochastic Gradient Methods. CoRR (2020)
Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
Off-Policy Interval Estimation with Lipschitz Value Iteration. CoRR (2020)
Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. ICLR (2020)
Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
Certified Monotonic Neural Networks. CoRR (2020)
Mao Ye, Tongzheng Ren, Qiang Liu
Stein Self-Repulsive Dynamics: Benefits From Past Samples. CoRR (2020)
Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu
Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. CoRR (2020)
Mao Ye, Lemeng Wu, Qiang Liu
Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. NeurIPS (2020)
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 (2020)
Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. CoRR (2020)
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CoRR (2020)
Zan Liu, Xihong Chen, Qiang Liu
Estimating Zenith Tropospheric Delay Based on GPT2w Model. IEEE Access 7 (2019)
Yihao Feng, Lihong Li, Qiang Liu
A Kernel Loss for Solving the Bellman Equation. CoRR (2019)
Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
Learning Belief Representations for Imitation Learning in POMDPs. CoRR (2019)
Dilin Wang, Qiang Liu
Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. ICML (2019)
Qiang Liu, Lemeng Wu, Dilin Wang
Splitting Steepest Descent for Growing Neural Architectures. CoRR (2019)
Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. CoRR (2019)
Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
Exploration via Hindsight Goal Generation. NeurIPS (2019)
Aishan Liu, Xianglong Liu, Chongzhi Zhang, Hang Yu, Qiang Liu, Junfeng He
Training Robust Deep Neural Networks via Adversarial Noise Propagation. CoRR (2019)
Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
Learning Belief Representations for Imitation Learning in POMDPs. UAI (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Qiang Liu