Liu, Qiang

189 publications

CVPR 2025 AMO Sampler: Enhancing Text Rendering with Overshooting Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu, Hongliang Fei

AAAI 2025 CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation Yuting Liu, Jinghao Zhang, Yizhou Dang, Yuliang Liang, Qiang Liu, Guibing Guo, Jianzhe Zhao, Xingwei Wang

ICLR 2025 ConFIG: Towards Conflict-Free Training of Physics Informed Neural Networks Qiang Liu, Mengyu Chu, Nils Thuerey

ICCV 2025 Improving Rectified Flow with Boundary Conditions Xixi Hu, Runlong Liao, Keyang Xu, Bo Liu, Yeqing Li, Eugene Ie, Hongliang Fei, Qiang Liu

CVPR 2025 InsightEdit: Towards Better Instruction Following for Image Editing Yingjing Xu, Jie Kong, Jiazhi Wang, Xiao Pan, Bo Lin, Qiang Liu

ICLR 2025 Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows Xiangxin Zhou, Yi Xiao, Haowei Lin, Xinheng He, Jiaqi Guan, Yang Wang, Qiang Liu, Feng Zhou, Liang Wang, Jianzhu Ma

ICCV 2025 LIRA: Inferring Segmentation in Large Multi-Modal Models with Local Interleaved Region Assistance Zhang Li, Biao Yang, Qiang Liu, Shuo Zhang, Zhiyin Ma, Liang Yin, Linger Deng, Yabo Sun, Yuliang Liu, Xiang Bai

ICLR 2025 Longhorn: State Space Models Are Amortized Online Learners Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu

AISTATS 2025 Memory-Efficient Optimization with Factorized Hamiltonian Descent Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu

ICLR 2025 MolSpectra: Pre-Training 3D Molecular Representation with Multi-Modal Energy Spectra Liang Wang, Shaozhen Liu, Yu Rong, Deli Zhao, Qiang Liu, Shu Wu, Liang Wang

ICML 2025 PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations Benjamin Holzschuh, Qiang Liu, Georg Kohl, Nils Thuerey

ICML 2025 PIPA: Preference Alignment as Prior-Informed Statistical Estimation Junbo Li, Zhangyang Wang, Qiang Liu

ICLR 2025 PN-GAIL: Leveraging Non-Optimal Information from Imperfect Demonstrations Qiang Liu, Huiqiao Fu, Kaiqiang Tang, Chunlin Chen, Daoyi Dong

NeurIPS 2025 Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing Junfei Wu, Jian Guan, Kaituo Feng, Qiang Liu, Shu Wu, Liang Wang, Wei Wu, Tieniu Tan

ICML 2025 SKIM: Any-Bit Quantization Pushing the Limits of Post-Training Quantization Runsheng Bai, Bo Liu, Qiang Liu

AAAI 2025 ST3: Accelerating Multimodal Large Language Model by Spatial-Temporal Visual Token Trimming Jiedong Zhuang, Lu Lu, Ming Dai, Rui Hu, Jian Chen, Qiang Liu, Haoji Hu

CVPR 2025 Steepest Descent Density Control for Compact 3D Gaussian Splatting Peihao Wang, Yuehao Wang, Dilin Wang, Sreyas Mohan, Zhiwen Fan, Lemeng Wu, Ruisi Cai, Yu-Ying Yeh, Zhangyang Wang, Qiang Liu, Rakesh Ranjan

AISTATS 2025 SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra

ICLR 2025 Uncovering Overfitting in Large Language Model Editing Mengqi Zhang, Xiaotian Ye, Qiang Liu, Shu Wu, Pengjie Ren, Zhumin Chen

ICML 2024 A Computational Framework for Solving Wasserstein Lagrangian Flows Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

NeurIPS 2024 AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies Xixi Hu, Bo Liu, Xingchao Liu, Qiang Liu

NeurIPS 2024 Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization Dingshuo Chen, Zhixun Li, Yuyan Ni, Guibin Zhang, Ding Wang, Qiang Liu, Shu Wu, Jeffrey Xu Yu, Liang Wang

NeurIPS 2024 Communication Efficient Distributed Training with Distributed Lion Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu

NeurIPS 2024 Enhancing Protein Mutation Effect Prediction Through a Retrieval-Augmented Framework Ruihan Guo, Rui Wang, Ruidong Wu, Zhizhou Ren, Jiahan Li, Shitong Luo, Zuofan Wu, Qiang Liu, Jian Peng, Jianzhu Ma

ICML 2024 Evolution-Inspired Loss Functions for Protein Representation Learning Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz

ICLRW 2024 Evolution-Inspired Loss Functions for Protein Representation Learning Chengyue Gong, Adam Klivans, James Madigan Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz

ICML 2024 FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng

AAAI 2024 Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables Haisong Gong, Weizhi Xu, Shu Wu, Qiang Liu, Liang Wang

ICLR 2024 InstaFlow: One Step Is Enough for High-Quality Diffusion-Based Text-to-Image Generation Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu

AAAI 2024 Layer Compression of Deep Networks with Straight Flows Chengyue Gong, Xiaocong Du, Bhargav Bhushanam, Lemeng Wu, Xingchao Liu, Dhruv Choudhary, Arun Kejariwal, Qiang Liu

ICLR 2024 Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu

NeurIPS 2024 Memory-Efficient LLM Training with Online Subspace Descent Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu

CVPR 2024 Monkey: Image Resolution and Text Label Are Important Things for Large Multi-Modal Models Zhang Li, Biao Yang, Qiang Liu, Zhiyin Ma, Shuo Zhang, Jingxu Yang, Yabo Sun, Yuliang Liu, Xiang Bai

NeurIPSW 2024 OnThePlanning Abilities of OpenAI’s O1 Models: Feasibility, Optimality, and Generalizability Kevin Wang, Junbo Li, Neel P. Bhatt, Yihan Xi, Qiang Liu, Ufuk Topcu, Zhangyang Wang

NeurIPS 2024 PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, Jiashi Feng

NeurIPS 2024 Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang

NeurIPS 2024 Quadratic Quantum Variational Monte Carlo Baiyu Su, Qiang Liu

AAAI 2024 Rethinking Graph Masked Autoencoders Through Alignment and Uniformity Liang Wang, Xiang Tao, Qiang Liu, Shu Wu, Liang Wang

ECCV 2024 SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow Yuanzhi Zhu, Xingchao Liu, Qiang Liu

ECCV 2024 Solving Motion Planning Tasks with a Scalable Generative Model Yihan Hu, Siqi Chai, Zhening Yang, Jingyu Qian, Kun Li, Wenxin Shao, Haichao Zhang, Wei Xu, Qiang Liu

CVPR 2024 Taming Mode Collapse in Score Distillation for Text-to-3D Generation Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra

AAAI 2024 Text-Guided Molecule Generation with Diffusion Language Model Haisong Gong, Qiang Liu, Shu Wu, Liang Wang

ICMLW 2024 Uncertainty-Aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models Qiang Liu, Nils Thuerey

NeurIPS 2024 VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark Han Huang, Haitian Zhong, Tao Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

NeurIPSW 2023 A Computational Framework for Solving Wasserstein Lagrangian Flows Kirill Neklyudov, Rob Brekelmans, Alexander Tong, Lazar Atanackovic, Qiang Liu, Alireza Makhzani

CVPRW 2023 Assigned MURA Defect Generation Based on Diffusion Model Weizhi Liu, Chang Liu, Qiang Liu, Dahai Yu

NeurIPSW 2023 Binding Oracle: Fine-Tuning from Stability to Binding Free Energy Chengyue Gong, Adam Klivans, Jordan Wells, James Loy, Qiang Liu, Alex Dimakis, Daniel Diaz

ICML 2023 DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu

ICCV 2023 Efficient Transformer-Based 3D Object Detection with Dynamic Token Halting Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu

NeurIPS 2023 FAMO: Fast Adaptive Multitask Optimization Bo Liu, Yihao Feng, Peter Stone, Qiang Liu

CVPR 2023 Fast Point Cloud Generation with Straight Flows Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu

ICLR 2023 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow Xingchao Liu, Chengyue Gong, Qiang Liu

CVPR 2023 FlowGrad: Controlling the Output of Generative ODEs with Gradients Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu

NeurIPS 2023 GSLB: The Graph Structure Learning Benchmark Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Yu

ICLR 2023 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

CVPR 2023 Image as a Foreign Language: BEiT Pretraining for Vision and Vision-Language Tasks Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, Furu Wei

NeurIPS 2023 LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

NeurIPS 2023 Language Is Not All You Need: Aligning Perception with Language Models Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, Qiang Liu, Kriti Aggarwal, Zewen Chi, Nils Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, Furu Wei

ICLR 2023 Learning Diffusion Bridges on Constrained Domains Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu

AAAI 2023 Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

NeurIPSW 2023 Microenvironment Flows as Protein Engineers Chengyue Gong, Lemeng Wu, Daniel Diaz, Xingchao Liu, James Loy, Adam Klivans, Qiang Liu

ICML 2023 MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma

CVPR 2023 Planning-Oriented Autonomous Driving Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li, Chonghao Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang, Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, Hongyang Li

NeurIPSW 2023 RF-POLICY: Rectified Flows Are Computation-Adaptive Decision Makers Xixi Hu, Bo Liu, Xingchao Liu, Qiang Liu

ICLR 2023 Sampling with Mollified Interaction Energy Descent Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin, Justin Solomon

CVPR 2023 Sparsely Annotated Semantic Segmentation with Adaptive Gaussian Mixtures Linshan Wu, Zhun Zhong, Leyuan Fang, Xingxin He, Qiang Liu, Jiayi Ma, Hao Chen

NeurIPS 2023 Uncovering Neural Scaling Laws in Molecular Representation Learning Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, Zhixun Li, Qiang Liu, Shu Wu, Liang Wang

NeurIPS 2023 Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation Kirill Neklyudov, Jannes Nys, Luca Thiede, Juan Carrasquilla, Qiang Liu, Max Welling, Alireza Makhzani

ICML 2022 A Langevin-like Sampler for Discrete Distributions Ruqi Zhang, Xingchao Liu, Qiang Liu

LoG 2022 A Survey on Deep Graph Generation: Methods and Applications Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu

AAAI 2022 AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Clouds Yihan Hu, Zhuangzhuang Ding, Runzhou Ge, Wenxin Shao, Li Huang, Kun Li, Qiang Liu

NeurIPS 2022 BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach Bo Liu, Mao Ye, Stephen Wright, Peter Stone, Qiang Liu

NeurIPSW 2022 BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach Mao Ye, Bo Liu, Stephen Wright, Peter Stone, Qiang Liu

ECML-PKDD 2022 Bi-Directional Contrastive Distillation for Multi-Behavior Recommendation Yabo Chu, Enneng Yang, Qiang Liu, Yuting Liu, Linying Jiang, Guibing Guo

ACML 2022 Bootstrapping a High Quality Multilingual Multimodal Dataset for Bletchley Owais Khan Mohammed, Kriti Aggarwal, Qiang Liu, Saksham Singhal, Johan Bjorck, Subhojit Som

ICML 2022 Centroid Approximation for Bootstrap: Improving Particle Quality at Inference Mao Ye, Qiang Liu

CoLLAs 2022 Continual Learning and Private Unlearning Bo Liu, Qiang Liu, Peter Stone

ECCVW 2022 Depth Completion Using Laplacian Pyramid-Based Depth Residuals Haosong Yue, Qiang Liu, Zhong Liu, Jing Zhang, Xingming Wu

NeurIPS 2022 Diffusion-Based Molecule Generation with Informative Prior Bridges Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu

NeurIPSW 2022 Diffusion-Based Molecule Generation with Informative Prior Bridges Chengyue Gong, Lemeng Wu, Xingchao Liu, Mao Ye, Qiang Liu

ECCVW 2022 Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 Challenge: Report Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

ICLR 2022 Energy-Inspired Molecular Conformation Optimization Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng

ICMLW 2022 Featurizations Matter: A Multiview Contrastive Learning Approach to Molecular Pretraining Yanqiao Zhu, Dingshuo Chen, Yuanqi Du, Yingze Wang, Qiang Liu, Shu Wu

NeurIPS 2022 First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data Mao Ye, Lemeng Wu, Qiang Liu

NeurIPSW 2022 First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data Mao Ye, Lemeng Wu, Qiang Liu

NeurIPSW 2022 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow Xingchao Liu, Chengyue Gong, Qiang Liu

UAI 2022 Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu

IJCAI 2022 GraphDIVE: Graph Classification by Mixture of Diverse Experts Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

NeurIPSW 2022 HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew Ellington, Alex Dimakis, Adam Klivans

ICML 2022 How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity Chengyue Gong, Lemeng Wu, Qiang Liu

NeurIPSW 2022 Let Us Build Bridges: Understanding and Extending Diffusion Generative Models Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu

ECCVW 2022 MIPI 2022 Challenge on RGB+ToF Depth Completion: Dataset and Report Wenxiu Sun, Qingpeng Zhu, Chongyi Li, Ruicheng Feng, Shangchen Zhou, Jun Jiang, Qingyu Yang, Chen Change Loy, Jinwei Gu, Dewang Hou, Kai Zhao, Liying Lu, Yu Li, Huaijia Lin, Ruizheng Wu, Jiangbo Lu, Jiaya Jia, Qiang Liu, Haosong Yue, Danyang Cao, Lehang Yu, Jiaxuan Quan, Jixiang Liang, Yufei Wang, Yuchao Dai, Peng Yang, Hu Yan, Houbiao Liu, Siyuan Su, Xuanhe Li, Rui Ren, Yunlong Liu, Yufan Zhu, Dong Lao, Alex Wong, Katie Chang

ICLR 2022 NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict Aware Supernet Training Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra

NeurIPSW 2022 Neural Volumetric Mesh Generator Yan Zheng, Lemeng Wu, Xingchao Liu, Zhen Chen, Qiang Liu, Qixing Huang

UAI 2022 Pareto Navigation Gradient Descent: A First-Order Algorithm for Optimization in Pareto Set Mao Ye, Qiang Liu

NeurIPSW 2022 Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations Yingtao Luo, Qiang Liu, Yuntian Chen, Wenbo Hu, Tian Tian, Jun Zhu

NeurIPS 2022 Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent Ruqi Zhang, Qiang Liu, Xin Tong

NeurIPS 2022 VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, Furu Wei

JMLR 2021 A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family Trambak Banerjee, Qiang Liu, Gourab Mukherjee, Wengunag Sun

CVPR 2021 AlphaMatch: Improving Consistency for Semi-Supervised Learning with Alpha-Divergence Chengyue Gong, Dilin Wang, Qiang Liu

ICML 2021 AlphaNet: Improved Training of Supernets with Alpha-Divergence Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

NeurIPS 2021 Argmax Centroid Chengyue Gong, Mao Ye, Qiang Liu

NeurIPS 2021 Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach Chengyue Gong, Xingchao Liu, Qiang Liu

ICML 2021 Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar

NeurIPS 2021 Conflict-Averse Gradient Descent for Multi-Task Learning Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

CVPR 2021 KeepAugment: A Simple Information-Preserving Data Augmentation Approach Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

CVPR 2021 MaxUp: Lightweight Adversarial Training with Data Augmentation Improves Neural Network Training Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu

ICLR 2021 Non-Asymptotic Confidence Intervals of Off-Policy Evaluation: Primal and Dual Bounds Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu

AAAI 2021 Post-Training Quantization with Multiple Points: Mixed Precision Without Mixed Precision Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu

NeurIPS 2021 Profiling Pareto Front with Multi-Objective Stein Variational Gradient Descent Xingchao Liu, Xin Tong, Qiang Liu

NeurIPS 2021 Sampling with Trusthworthy Constraints: A Variational Gradient Framework Xingchao Liu, Xin Tong, Qiang Liu

ICLR 2021 VCNet and Functional Targeted Regularization for Learning Causal Effects of Continuous Treatments Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

ICML 2020 A Chance-Constrained Generative Framework for Sequence Optimization Xianggen Liu, Qiang Liu, Sen Song, Jian Peng

ICML 2020 Accountable Off-Policy Evaluation with Kernel Bellman Statistics Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu

NeurIPS 2020 Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

ICLR 2020 Black-Box Off-Policy Estimation for Infinite-Horizon Reinforcement Learning Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou

NeurIPS 2020 Certified Monotonic Neural Networks Xingchao Liu, Xing Han, Na Zhang, Qiang Liu

ICLR 2020 Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu

ICLR 2020 Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu

NeurIPS 2020 Firefly Neural Architecture Descent: A General Approach for Growing Neural Networks Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu

ICML 2020 Go Wide, Then Narrow: Efficient Training of Deep Thin Networks Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc Le, Qiang Liu, Dale Schuurmans

ICML 2020 Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam Klivans, Qiang Liu

NeurIPS 2020 Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets Is Enough Mao Ye, Lemeng Wu, Qiang Liu

NeurIPS 2020 Implicit Regularization and Convergence for Weight Normalization Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel Ward, Qiang Liu

NeurIPS 2020 Off-Policy Interval Estimation with Lipschitz Value Iteration Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu

NeurIPS 2020 Stein Self-Repulsive Dynamics: Benefits from past Samples Mao Ye, Tongzheng Ren, Qiang Liu

AISTATS 2020 Stein Variational Inference for Discrete Distributions Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu

ICLR 2020 Unifying Graph Convolutional Networks as Matrix Factorization Zhaocheng Liu, Qiang Liu, Haoli Zhang, Jun Zhu

NeurIPS 2019 A Kernel Loss for Solving the Bellman Equation Yihao Feng, Lihong Li, Qiang Liu

NeurIPS 2019 Exploration via Hindsight Goal Generation Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng

ICML 2019 Improving Neural Language Modeling via Adversarial Training Dilin Wang, Chengyue Gong, Qiang Liu

UAI 2019 Learning Belief Representations for Imitation Learning in POMDPs Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng

ICLR 2019 Learning Self-Imitating Diverse Policies Tanmay Gangwani, Qiang Liu, Jian Peng

ICML 2019 Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models Dilin Wang, Qiang Liu

ICLR 2019 Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng

ICML 2019 Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization Chengyue Gong, Jian Peng, Qiang Liu

NeurIPS 2019 Regularization Matters: Generalization and Optimization of Neural Nets V.s. Their Induced Kernel Colin Wei, Jason Lee, Qiang Liu, Tengyu Ma

AAAI 2019 Robustness Can Be Cheap: A Highly Efficient Approach to Discover Outliers Under High Outlier Ratios Siqi Wang, En Zhu, Xiping Hu, Xinwang Liu, Qiang Liu, Jianping Yin, Fei Wang

NeurIPS 2019 Splitting Steepest Descent for Growing Neural Architectures Lemeng Wu, Dilin Wang, Qiang Liu

NeurIPS 2019 Stein Variational Gradient Descent with Matrix-Valued Kernels Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu

ICLR 2018 Action-Dependent Control Variates for Policy Optimization via Stein Identity Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

NeurIPS 2018 Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

IJCAI 2018 Efficient Localized Inference for Large Graphical Models Jinglin Chen, Jian Peng, Qiang Liu

IJCAI 2018 Energy-Efficient Amortized Inference with Cascaded Deep Classifiers Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng

ICML 2018 Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy Jiasen Yang, Qiang Liu, Vinayak Rao, Jennifer Neville

ICML 2018 Learning to Explore via Meta-Policy Gradient Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

IJCAI 2018 Multi-Agent Epistemic Planning with Common Knowledge Qiang Liu, Yongmei Liu

ICLR 2018 On the Discrimination-Generalization Tradeoff in GANs Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

ICML 2018 Stein Variational Gradient Descent Without Gradient Jun Han, Qiang Liu

NeurIPS 2018 Stein Variational Gradient Descent as Moment Matching Qiang Liu, Dilin Wang

ICML 2018 Stein Variational Message Passing for Continuous Graphical Models Dilin Wang, Zhe Zeng, Qiang Liu

NeurIPS 2018 Variational Inference with Tail-Adaptive F-Divergence Dilin Wang, Hao Liu, Qiang Liu

IJCAI 2017 A Convolutional Approach for Misinformation Identification Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

AISTATS 2017 Black-Box Importance Sampling Qiang Liu, Jason D. Lee

JMLR 2017 Communication-Efficient Sparse Regression Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor

UAI 2017 Learning to Draw Samples with Amortized Stein Variational Gradient Descent Yihao Feng, Dilin Wang, Qiang Liu

AISTATS 2017 Local Perturb-and-MAP for Structured Prediction Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi

AAAI 2017 Reference Based LSTM for Image Captioning Minghai Chen, Guiguang Ding, Sicheng Zhao, Hui Chen, Qiang Liu, Jungong Han

UAI 2017 Stein Variational Adaptive Importance Sampling Jun Han, Qiang Liu

NeurIPS 2017 Stein Variational Gradient Descent as Gradient Flow Qiang Liu

UAI 2017 Stein Variational Policy Gradient Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng

ICML 2016 A Kernelized Stein Discrepancy for Goodness-of-Fit Tests Qiang Liu, Jason Lee, Michael Jordan

NeurIPS 2016 Bootstrap Model Aggregation for Distributed Statistical Learning Jun Han, Qiang Liu

UAI 2016 Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds Dilin Wang, John W. Fisher Iii, Qiang Liu

UAI 2016 Importance Weighted Consensus Monte Carlo for Distributed Bayesian Inference Qiang Liu

AAAI 2016 Information Credibility Evaluation on Social Media Shu Wu, Qiang Liu, Yong Liu, Liang Wang, Tieniu Tan

NeurIPS 2016 Learning Infinite RBMs with Frank-Wolfe Wei Ping, Qiang Liu, Alex Ihler

AAAI 2016 Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan

AAAI 2016 SAPE: A System for Situation-Aware Public Security Evaluation Shu Wu, Qiang Liu, Ping Bai, Liang Wang, Tieniu Tan

NeurIPS 2016 Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu, Dilin Wang

AAAI 2015 COT: Contextual Operating Tensor for Context-Aware Recommender Systems Qiang Liu, Shu Wu, Liang Wang

NeurIPS 2015 Decomposition Bounds for Marginal MAP Wei Ping, Qiang Liu, Alex Ihler

UAI 2015 Estimating the Partition Function by Discriminance Sampling Qiang Liu, Jian Peng, Alexander Ihler, John W. Fisher Iii

NeurIPS 2015 Probabilistic Variational Bounds for Graphical Models Qiang Liu, John W. Fisher Iii, Alex Ihler

ICML 2014 Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy Dengyong Zhou, Qiang Liu, John Platt, Christopher Meek

NeurIPS 2014 Distributed Estimation, Information Loss and Exponential Families Qiang Liu, Alex Ihler

ICML 2014 Marginal Structured SVM with Hidden Variables Wei Ping, Qiang Liu, Alex Ihler

NeurIPS 2013 Scoring Workers in Crowdsourcing: How Many Control Questions Are Enough? Qiang Liu, Alex Ihler, Mark Steyvers

JMLR 2013 Using Symmetry and Evolutionary Search to Minimize Sorting Networks Qiang Liu, Alexander Ihler

NeurIPS 2013 Variational Planning for Graph-Based MDPs Qiang Cheng, Qiang Liu, Feng Chen, Alex Ihler

UAI 2012 Belief Propagation for Structured Decision Making Qiang Liu, Alexander Ihler

ICML 2012 Distributed Parameter Estimation via Pseudo-Likelihood Qiang Liu, Alexander Ihler

NeurIPS 2012 Variational Inference for Crowdsourcing Qiang Liu, Jian Peng, Alex Ihler

ICML 2011 Bounding the Partition Function Using Holder's Inequality Qiang Liu, Alexander Ihler

AISTATS 2011 Learning Scale Free Networks by Reweighted $\ell_1$ Regularization Qiang Liu, Alexander Ihler

UAI 2011 Variational Algorithms for Marginal MAP Qiang Liu, Alexander Ihler

AISTATS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth

UAI 2010 Negative Tree Reweighted Belief Propagation Qiang Liu, Alexander Ihler

ICML 2010 Particle Filtered MCMC-MLE with Connections to Contrastive Divergence Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth