Qiang Liu - RankMe

257 papers A* 84A 21C 1Journal 129Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Gumbel Distillation for Parallel Text Generation. CoRR Chi Zhang, Xixi Hu, Bo Liu, Qiang Liu
2026	J	`jnl`	∇-Reasoner: LLM Reasoning via Test-Time Gradient Descent in Latent Space. CoRR Peihao Wang, Ruisi Cai, Zhen Wang, Hongyuan Mei, Qiang Liu, Pan Li, Zhangyang Wang
2025	A*	`conf`	AMO Sampler: Enhancing Text Rendering with Overshooting. CVPR Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu, Hongliang Fei
2025	J	`jnl`	Improving Adaptive Moment Optimization via Preconditioner Diagonalization. CoRR Son Nguyen, Bo Liu, Lizhang Chen, Qiang Liu
2025	J	`jnl`	Improving Rectified Flow with Boundary Conditions. CoRR Xixi Hu, Runlong Liao, Keyang Xu, Bo Liu, Yeqing Li, Eugene Ie, Hongliang Fei, Qiang Liu
2025	A*	`conf`	Longhorn: State Space Models are Amortized Online Learners. ICLR Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu
2025	A	`conf`	Memory-Efficient Optimization with Factorized Hamiltonian Descent. AISTATS Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu
2025	A*	`conf`	PIPA: Preference Alignment as Prior-Informed Statistical Estimation. ICML Junbo Li, Zhangyang Wang, Qiang Liu
2025	J	`jnl`	PIPA: Preference Alignment as Prior-Informed Statistical Estimation. CoRR Junbo Li, Zhangyang Wang, Qiang Liu
2025	J	`jnl`	RoboSSM: Scalable In-context Imitation Learning via State-Space Models. CoRR Youngju Yoo, Jiaheng Hu, Yifeng Zhu, Bo Liu, Qiang Liu, Roberto Martín-Martín, Peter Stone
2025	A*	`conf`	SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization. ICML Runsheng Bai, Bo Liu, Qiang Liu
2025	A*	`conf`	Steepest Descent Density Control for Compact 3D Gaussian Splatting. CVPR Peihao Wang, Yuehao Wang, Dilin Wang, Sreyas Mohan, Zhiwen Fan, Lemeng Wu, Ruisi Cai, Yu-Ying Yeh, Zhangyang Wang, Qiang Liu, Rakesh Ranjan
2025	J	`jnl`	Steepest Descent Density Control for Compact 3D Gaussian Splatting. CoRR Peihao Wang, Yuehao Wang, Dilin Wang, Sreyas Mohan, Zhiwen Fan, Lemeng Wu, Ruisi Cai, Yu-Ying Yeh, Zhangyang Wang, Qiang Liu, Rakesh Ranjan
2025	A	`conf`	SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity. AISTATS Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
2024	J	`jnl`	AMO Sampler: Enhancing Text Rendering with Overshooting. CoRR Xixi Hu, Keyang Xu, Bo Liu, Qiang Liu, Hongliang Fei
2024	A*	`conf`	AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies. NeurIPS Xixi Hu, Qiang Liu, Xingchao Liu, Bo Liu
2024	J	`jnl`	AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies. CoRR Xixi Hu, Bo Liu, Xingchao Liu, Qiang Liu
2024	J	`jnl`	Cautious Optimizers: Improving Training with One Line of Code. CoRR Kaizhao Liang, Lizhang Chen, Bo Liu, Qiang Liu
2024	A*	`conf`	Communication Efficient Distributed Training with Distributed Lion. NeurIPS Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu
2024	J	`jnl`	Communication Efficient Distributed Training with Distributed Lion. CoRR Bo Liu, Lemeng Wu, Lizhang Chen, Kaizhao Liang, Jiaxu Zhu, Chen Liang, Raghuraman Krishnamoorthi, Qiang Liu
2024	A*	`conf`	Enhancing Protein Mutation Effect Prediction through a Retrieval-Augmented Framework. NeurIPS Ruihan Guo, Rui Wang, Ruidong Wu, Zhizhou Ren, Jiahan Li, Shitong Luo, Zuofan Wu, Qiang Liu, Jian Peng, Jianzhu Ma
2024	A*	`conf`	Evolution-Inspired Loss Functions for Protein Representation Learning. ICML Chengyue Gong, Adam R. Klivans, James Loy, Tianlong Chen, Qiang Liu, Daniel Jesus Diaz
2024	A*	`conf`	FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames. ICML Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng
2024	J	`jnl`	FAFE: Immune Complex Modeling with Geodesic Distance Loss on Noisy Group Frames. CoRR Ruidong Wu, Ruihan Guo, Rui Wang, Shitong Luo, Yue Xu, Jiahan Li, Jianzhu Ma, Qiang Liu, Yunan Luo, Jian Peng
2024	J	`jnl`	Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting. CoRR Bo Liu, Mao Ye, Peter Stone, Qiang Liu
2024	J	`jnl`	H-Fac: Memory-Efficient Optimization with Factorized Hamiltonian Descent. CoRR Son Nguyen, Lizhang Chen, Bo Liu, Qiang Liu
2024	A*	`conf`	InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation. ICLR Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu
2024	A*	`conf`	Layer Compression of Deep Networks with Straight Flows. AAAI Chengyue Gong, Xiaocong Du, Bhargav Bhushanam, Lemeng Wu, Xingchao Liu, Dhruv Choudhary, Arun Kejariwal, Qiang Liu
2024	A*	`conf`	Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts. ICLR Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu
2024	J	`jnl`	Longhorn: State Space Models are Amortized Online Learners. CoRR Bo Liu, Rui Wang, Lemeng Wu, Yihao Feng, Peter Stone, Qiang Liu
2024	A*	`conf`	Memory-Efficient LLM Training with Online Subspace Descent. NeurIPS Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu
2024	J	`jnl`	Memory-Efficient LLM Training with Online Subspace Descent. CoRR Kaizhao Liang, Bo Liu, Lizhang Chen, Qiang Liu
2024	J	`jnl`	On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability. CoRR Kevin Wang, Junbo Li, Neel P. Bhatt, Yihan Xi, Qiang Liu, Ufuk Topcu, Zhangyang Wang
2024	—	`conf`	PathFusion: Path-Consistent Lidar-Camera Deep Feature Fusion. 3DV Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra
2024	A*	`conf`	PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator. NeurIPS Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, Jiashi Feng
2024	J	`jnl`	PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator. CoRR Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew, Qiang Liu, Jiashi Feng
2024	J	`jnl`	Rectified Flow For Structure Based Drug Design. CoRR Daiheng Zhang, Chengyue Gong, Qiang Liu
2024	J	`jnl`	SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization. CoRR Runsheng Bai, Bo Liu, Qiang Liu
2024	—	`conf`	SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow. ECCV (82) Yuanzhi Zhu, Xingchao Liu, Qiang Liu
2024	J	`jnl`	SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow. CoRR Yuanzhi Zhu, Xingchao Liu, Qiang Liu
2024	J	`jnl`	SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity. CoRR Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
2024	A*	`conf`	Taming Mode Collapse in Score Distillation for Text-to-3D Generation. CVPR Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
2024	J	`jnl`	Taming Mode Collapse in Score Distillation for Text-to-3D Generation. CoRR Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
2023	A*	`conf`	Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. ICCV Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
2023	J	`jnl`	Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. CoRR Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu
2023	A*	`conf`	FAMO: Fast Adaptive Multitask Optimization. NeurIPS Bo Liu, Yihao Feng, Peter Stone, Qiang Liu
2023	J	`jnl`	FAMO: Fast Adaptive Multitask Optimization. CoRR Bo Liu, Yihao Feng, Peter Stone, Qiang Liu
2023	A*	`conf`	Fast Point Cloud Generation with Straight Flows. CVPR Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu
2023	A*	`conf`	Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. ICLR Xingchao Liu, Chengyue Gong, Qiang Liu
2023	A*	`conf`	FlowGrad: Controlling the Output of Generative ODEs with Gradients. CVPR Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu
2023	A*	`conf`	HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. ICLR Tianlong Chen, Chengyue Gong, Daniel Jesus Diaz, Xuxi Chen, Jordan Tyler Wells, Qiang Liu, Zhangyang Wang, Andrew D. Ellington, Alex Dimakis, Adam R. Klivans
2023	J	`jnl`	InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation. CoRR Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu
2023	A*	`conf`	LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning. NeurIPS Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone
2023	J	`jnl`	LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning. CoRR Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone
2023	J	`jnl`	LLM+P: Empowering Large Language Models with Optimal Planning Proficiency. CoRR Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, Peter Stone
2023	A*	`conf`	Learning Diffusion Bridges on Constrained Domains. ICLR Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu
2023	J	`jnl`	Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts. CoRR Lizhang Chen, Bo Liu, Kaizhao Liang, Qiang Liu
2023	A*	`conf`	Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning. AAAI Bo Liu, Yihao Feng, Qiang Liu, Peter Stone
2023	A*	`conf`	MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. ICML Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
2023	J	`jnl`	MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. CoRR Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
2022	A*	`conf`	A Langevin-like Sampler for Discrete Distributions. ICML Ruqi Zhang, Xingchao Liu, Qiang Liu
2022	J	`jnl`	A Langevin-like Sampler for Discrete Distributions. CoRR Ruqi Zhang, Xingchao Liu, Qiang Liu
2022	A*	`conf`	BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. NeurIPS Bo Liu, Mao Ye, Stephen Wright, Peter Stone, Qiang Liu
2022	J	`jnl`	BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. CoRR Mao Ye, Bo Liu, Stephen Wright, Peter Stone, Qiang Liu
2022	A*	`conf`	Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. ICML Mao Ye, Qiang Liu
2022	—	`conf`	Continual Learning and Private Unlearning. CoLLAs Bo Liu, Qiang Liu, Peter Stone
2022	J	`jnl`	Continual Learning and Private Unlearning. CoRR Bo Liu, Qiang Liu, Peter Stone
2022	A*	`conf`	Diffusion-based Molecule Generation with Informative Prior Bridges. NeurIPS Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu
2022	J	`jnl`	Diffusion-based Molecule Generation with Informative Prior Bridges. CoRR Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu
2022	A*	`conf`	Energy-Inspired Molecular Conformation Optimization. ICLR Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
2022	J	`jnl`	Fast Point Cloud Generation with Straight Flows. CoRR Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu
2022	A*	`conf`	First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data. NeurIPS Mao Ye, Lemeng Wu, Qiang Liu
2022	J	`jnl`	First Hitting Diffusion Models. CoRR Mao Ye, Lemeng Wu, Qiang Liu
2022	J	`jnl`	Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. CoRR Xingchao Liu, Chengyue Gong, Qiang Liu
2022	J	`jnl`	Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems. CoRR Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu
2022	A	`conf`	Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. UAI Mao Ye, Ruichen Jiang, Haoxiang Wang, Dhruv Choudhary, Xiaocong Du, Bhargav Bhushanam, Aryan Mokhtari, Arun Kejariwal, Qiang Liu
2022	—	`conf`	Harmless Transfer Learning for Item Embeddings. NAACL-HLT (Findings) Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal
2022	A*	`conf`	How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. ICML Chengyue Gong, Lemeng Wu, Qiang Liu
2022	J	`jnl`	How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. CoRR Chengyue Gong, Lemeng Wu, Qiang Liu
2022	J	`jnl`	Human as a Service: Towards Resilient Parking Search System With Sensorless Sensing. IEEE Trans. Intell. Transp. Syst. Di Wu, Zhanxiu Zeng, Fengrui Shi, Weiren Yu, Tao Wu, Qiang Liu
2022	J	`jnl`	Let us Build Bridges: Understanding and Extending Diffusion Generative Models. CoRR Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu
2022	J	`jnl`	Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning. CoRR Bo Liu, Yihao Feng, Qiang Liu, Peter Stone
2022	A*	`conf`	NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training. ICLR Chengyue Gong, Dilin Wang, Meng Li, Xinlei Chen, Zhicheng Yan, Yuandong Tian, Qiang Liu, Vikas Chandra
2022	J	`jnl`	Neural Volumetric Mesh Generator. CoRR Yan Zheng, Lemeng Wu, Xingchao Liu, Zhen Chen, Qiang Liu, Qixing Huang
2022	J	`jnl`	Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning. CoRR Ziyang Tang, Yihao Feng, Qiang Liu
2022	A	`conf`	Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. UAI Mao Ye, Qiang Liu
2022	J	`jnl`	PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion. CoRR Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra
2022	A*	`conf`	Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. NeurIPS Ruqi Zhang, Qiang Liu, Xin T. Tong
2022	J	`jnl`	Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. CoRR Ruqi Zhang, Qiang Liu, Xin T. Tong
2022	J	`jnl`	Split Localized Conformal Prediction. CoRR Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu
2021	A*	`conf`	AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. CVPR Chengyue Gong, Dilin Wang, Qiang Liu
2021	J	`jnl`	AlphaNet: Improved Training of Supernet with Alpha-Divergence. CoRR Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
2021	A*	`conf`	AlphaNet: Improved Training of Supernets with Alpha-Divergence. ICML Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
2021	A*	`conf`	Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach. NeurIPS Chengyue Gong, Xingchao Liu, Qiang Liu
2021	J	`jnl`	Centroid Approximation for Bootstrap. CoRR Mao Ye, Qiang Liu
2021	J	`jnl`	Centroid Transformers: Learning to Abstract with Attention. CoRR Lemeng Wu, Xingchao Liu, Qiang Liu
2021	J	`jnl`	Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team Composition. CoRR Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Animashree Anandkumar
2021	A*	`conf`	Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. ICML Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar
2021	J	`jnl`	Conflict-Averse Gradient Descent for Multi-task Learning. CoRR Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
2021	A*	`conf`	Conflict-Averse Gradient Descent for Multi-task learning. NeurIPS Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
2021	J	`jnl`	Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. Pattern Recognit. Xiao Bai, Xiang Wang, Xianglong Liu, Qiang Liu, Jingkuan Song, Nicu Sebe, Been Kim
2021	J	`jnl`	Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. CoRR Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu
2021	J	`jnl`	FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization. CoRR Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu
2021	J	`jnl`	Improve Vision Transformers Training by Suppressing Over-smoothing. CoRR Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
2021	A*	`conf`	KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CVPR Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
2021	A*	`conf`	MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training. CVPR Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu
2021	A*	`conf`	Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. ICLR Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
2021	J	`jnl`	Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. CoRR Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu
2021	J	`jnl`	Pareto Navigation Gradient Descent: a First-Order Algorithm for Optimization in Pareto Set. CoRR Mao Ye, Qiang Liu
2021	A*	`conf`	Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. AAAI Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu
2021	A*	`conf`	Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent. NeurIPS Xingchao Liu, Xin Tong, Qiang Liu
2021	A*	`conf`	Sampling with Trusthworthy Constraints: A Variational Gradient Framework. NeurIPS Xingchao Liu, Xin Tong, Qiang Liu
2021	J	`jnl`	Speeding up Deep Model Training by Sharing Weights and Then Unsharing. CoRR Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou
2021	J	`jnl`	Training Robust Deep Neural Networks via Adversarial Noise Propagation. IEEE Trans. Image Process. Aishan Liu, Xianglong Liu, Hang Yu, Chongzhi Zhang, Qiang Liu, Dacheng Tao
2021	A*	`conf`	VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. ICLR Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae
2021	J	`jnl`	VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. CoRR Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae
2021	A*	`conf`	argmax centroid. NeurIPS Chengyue Gong, Mao Ye, Qiang Liu
2020	A*	`conf`	A Chance-Constrained Generative Framework for Sequence Optimization. ICML Xianggen Liu, Qiang Liu, Sen Song, Jian Peng
2020	J	`jnl`	Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations. Stat. Comput. Qiang Liu, Xin T. Tong
2020	A*	`conf`	Accountable Off-Policy Evaluation With Kernel Bellman Statistics. ICML Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
2020	J	`jnl`	Accountable Off-Policy Evaluation With Kernel Bellman Statistics. CoRR Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu
2020	J	`jnl`	Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data. CoRR Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, ZeLiang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal
2020	J	`jnl`	AlphaMatch: Improving Consistency for Semi-supervised Learning with Alpha-divergence. CoRR Chengyue Gong, Dilin Wang, Qiang Liu
2020	A*	`conf`	Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
2020	J	`jnl`	Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
2020	A*	`conf`	Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. ICLR Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
2020	J	`jnl`	Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. CoRR Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
2020	A*	`conf`	Certified Monotonic Neural Networks. NeurIPS Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
2020	J	`jnl`	Certified Monotonic Neural Networks. CoRR Xingchao Liu, Xing Han, Na Zhang, Qiang Liu
2020	J	`jnl`	Dimension Independent Generalization Error with Regularized Online Optimization. CoRR Xi Chen, Qiang Liu, Xin T. Tong
2020	A*	`conf`	Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. ICLR Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
2020	J	`jnl`	Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems. IEEE Trans. Intell. Transp. Syst. Di Wu, Lambros Lambrinos, Thomas Przepiorka, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann
2020	A*	`conf`	Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. NeurIPS Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu
2020	A*	`conf`	Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans
2020	J	`jnl`	Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans
2020	A*	`conf`	Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. ICML Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu
2020	J	`jnl`	Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. CoRR Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu
2020	A*	`conf`	Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. NeurIPS Mao Ye, Lemeng Wu, Qiang Liu
2020	J	`jnl`	Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. CoRR Mao Ye, Lemeng Wu, Qiang Liu
2020	A*	`conf`	Implicit Regularization and Convergence for Weight Normalization. NeurIPS Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu
2020	J	`jnl`	KeepAugment: A Simple Information-Preserving Data Augmentation Approach. CoRR Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
2020	J	`jnl`	MaxUp: A Simple Way to Improve Generalization of Neural Network Training. CoRR Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu
2020	A*	`conf`	Off-Policy Interval Estimation with Lipschitz Value Iteration. NeurIPS Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
2020	J	`jnl`	Off-Policy Interval Estimation with Lipschitz Value Iteration. CoRR Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu
2020	J	`jnl`	Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. CoRR Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu
2020	A*	`conf`	SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. ACL Mao Ye, Chengyue Gong, Qiang Liu
2020	J	`jnl`	SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. CoRR Mao Ye, Chengyue Gong, Qiang Liu
2020	J	`jnl`	Statistical Adaptive Stochastic Gradient Methods. CoRR Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao
2020	J	`jnl`	Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. CoRR Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu
2020	A*	`conf`	Stein Self-Repulsive Dynamics: Benefits From Past Samples. NeurIPS Mao Ye, Tongzheng Ren, Qiang Liu
2020	J	`jnl`	Stein Self-Repulsive Dynamics: Benefits From Past Samples. CoRR Mao Ye, Tongzheng Ren, Qiang Liu
2020	A	`conf`	Stein Variational Inference for Discrete Distributions. AISTATS Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu
2020	J	`jnl`	Stein Variational Inference for Discrete Distributions. CoRR Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu
2019	A*	`conf`	A Kernel Loss for Solving the Bellman Equation. NeurIPS Yihao Feng, Lihong Li, Qiang Liu
2019	J	`jnl`	A Kernel Loss for Solving the Bellman Equation. CoRR Yihao Feng, Lihong Li, Qiang Liu
2019	J	`jnl`	Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. CoRR Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
2019	J	`jnl`	Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent. CoRR Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu
2019	J	`jnl`	Estimating Zenith Tropospheric Delay Based on GPT2w Model. IEEE Access Zan Liu, Xihong Chen, Qiang Liu
2019	A*	`conf`	Exploration via Hindsight Goal Generation. NeurIPS Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
2019	J	`jnl`	Exploration via Hindsight Goal Generation. CoRR Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng
2019	J	`jnl`	Implicit Regularization of Normalization Methods. CoRR Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu
2019	A*	`conf`	Improving Neural Language Modeling via Adversarial Training. ICML Dilin Wang, Chengyue Gong, Qiang Liu
2019	J	`jnl`	Improving Neural Language Modeling via Adversarial Training. CoRR Dilin Wang, Chengyue Gong, Qiang Liu
2019	A	`conf`	Learning Belief Representations for Imitation Learning in POMDPs. UAI Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
2019	J	`jnl`	Learning Belief Representations for Imitation Learning in POMDPs. CoRR Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
2019	—	`conf`	Learning Self-Imitating Diverse Policies. ICLR (Poster) Tanmay Gangwani, Qiang Liu, Jian Peng
2019	—	`conf`	LithoROC: lithography hotspot detection with explicit ROC optimization. ASP-DAC Wei Ye, Yibo Lin, Meng Li, Qiang Liu, David Z. Pan
2019	A	`conf`	Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning. ICCAD Chengyue Gong, Zixuan Jiang, Dilin Wang, Yibo Lin, Qiang Liu, David Z. Pan
2019	A*	`conf`	Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. ICML Dilin Wang, Qiang Liu
2019	—	`conf`	Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. ICLR (Poster) Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng
2019	J	`jnl`	ParkCrowd: Reliable Crowdsensing for Aggregation and Dissemination of Parking Space Information. IEEE Trans. Intell. Transp. Syst. Fengrui Shi, Di Wu, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann
2019	A*	`conf`	Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization. ICML Chengyue Gong, Jian Peng, Qiang Liu
2019	A*	`conf`	Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. NeurIPS Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma
2019	J	`jnl`	Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning. CoRR Yang Liu, Yunan Luo, Yuanyi Zhong, Xi Chen, Qiang Liu, Jian Peng
2019	A*	`conf`	Splitting Steepest Descent for Growing Neural Architectures. NeurIPS Lemeng Wu, Dilin Wang, Qiang Liu
2019	J	`jnl`	Splitting Steepest Descent for Growing Neural Architectures. CoRR Qiang Liu, Lemeng Wu, Dilin Wang
2019	A*	`conf`	Stein Variational Gradient Descent With Matrix-Valued Kernels. NeurIPS Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu
2019	J	`jnl`	Stein Variational Gradient Descent With Matrix-Valued Kernels. CoRR Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu
2019	J	`jnl`	Training Robust Deep Neural Networks via Adversarial Noise Propagation. CoRR Aishan Liu, Xianglong Liu, Chongzhi Zhang, Hang Yu, Qiang Liu, Junfeng He
2018	—	`conf`	Action-dependent Control Variates for Policy Optimization via Stein Identity. ICLR (Poster) Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu
2018	—	`conf`	An Optimization View on Dynamic Routing Between Capsules. ICLR (Workshop) Dilin Wang, Qiang Liu
2018	A*	`conf`	Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. NeurIPS Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
2018	J	`jnl`	Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. CoRR Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
2018	A*	`conf`	Efficient Localized Inference for Large Graphical Models. IJCAI Jinglin Chen, Jian Peng, Qiang Liu
2018	A*	`conf`	Energy-efficient Amortized Inference with Cascaded Deep Classifiers. IJCAI Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng
2018	J	`jnl`	Learning Self-Imitating Diverse Policies. CoRR Tanmay Gangwani, Qiang Liu, Jian Peng
2018	A*	`conf`	Learning to Explore via Meta-Policy Gradient. ICML Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng
2018	J	`jnl`	Learning to Explore with Meta-Policy Gradient. CoRR Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng
2018	J	`jnl`	Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. CoRR Yuan Xie, Boyi Liu, Qiang Liu, Zhaoran Wang, Yuan Zhou, Jian Peng
2018	—	`conf`	On the Discrimination-Generalization Tradeoff in GANs. ICLR (Poster) Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He
2018	J	`jnl`	On the Margin Theory of Feedforward Neural Networks. CoRR Colin Wei, Jason D. Lee, Qiang Liu, Tengyu Ma
2018	A*	`conf`	Stein Variational Gradient Descent Without Gradient. ICML Jun Han, Qiang Liu
2018	J	`jnl`	Stein Variational Gradient Descent Without Gradient. CoRR Jun Han, Qiang Liu
2018	A*	`conf`	Stein Variational Gradient Descent as Moment Matching. NeurIPS Qiang Liu, Dilin Wang
2018	J	`jnl`	Stein Variational Gradient Descent as Moment Matching. CoRR Qiang Liu, Dilin Wang
2018	A*	`conf`	Stein Variational Message Passing for Continuous Graphical Models. ICML Dilin Wang, Zhe Zeng, Qiang Liu
2018	A*	`conf`	Variational Inference with Tail-adaptive f-Divergence. NeurIPS Dilin Wang, Hao Liu, Qiang Liu
2018	J	`jnl`	Variational Inference with Tail-adaptive f-Divergence. CoRR Dilin Wang, Hao Liu, Qiang Liu
2017	A	`conf`	Black-box Importance Sampling. AISTATS Qiang Liu, Jason D. Lee
2017	J	`jnl`	Communication-efficient Sparse Regression. J. Mach. Learn. Res. Jason D. Lee, Qiang Liu, Yuekai Sun, Jonathan E. Taylor
2017	A	`conf`	DeepOpp: Context-Aware Mobile Access to Social Media Content on Underground Metro Systems. ICDCS Di Wu, Dmitri I. Arkhipov, Thomas Przepiorka, Qiang Liu, Julie A. McCann, Amelia C. Regan
2017	J	`jnl`	Efficient Localized Inference for Large Graphical Models. CoRR Jinglin Chen, Jian Peng, Qiang Liu
2017	J	`jnl`	Energy-efficient Amortized Inference with Cascaded Deep Classifiers. CoRR Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng
2017	J	`jnl`	From Intermittent to Ubiquitous: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. Di Wu, Dmitri I. Arkhipov, Thomas Przepiorka, Yong Li, Bin Guo, Qiang Liu
2017	J	`jnl`	Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE. CoRR Qiang Liu, Dilin Wang
2017	J	`jnl`	Learning Infinite RBMs with Frank-Wolfe. CoRR Wei Ping, Qiang Liu, Alexander Ihler
2017	A	`conf`	Learning to Draw Samples with Amortized Stein Variational Gradient Descent. UAI Yihao Feng, Dilin Wang, Qiang Liu
2017	A	`conf`	Local Perturb-and-MAP for Structured Prediction. AISTATS Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi
2017	J	`jnl`	On the Discrimination-Generalization Tradeoff in GANs. CoRR Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He
2017	J	`jnl`	Sample-efficient Policy Optimization with Stein Control Variate. CoRR Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu
2017	A	`conf`	Stein Variational Adaptive Importance Sampling. UAI Jun Han, Qiang Liu
2017	A	`conf`	Stein Variational Policy Gradient. UAI Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng
2017	J	`jnl`	Stein Variational Policy Gradient. CoRR Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng
2017	J	`jnl`	Stochastic Variance Reduction for Policy Gradient Estimation. CoRR Tianbing Xu, Qiang Liu, Jian Peng
2017	J	`jnl`	Structured Stein Variational Inference for Continuous Graphical Models. CoRR Dilin Wang, Zhe Zeng, Qiang Liu
2016	A*	`conf`	A Kernelized Stein Discrepancy for Goodness-of-fit Tests. ICML Qiang Liu, Jason D. Lee, Michael I. Jordan
2016	J	`jnl`	Adaptive Lookup of Open WiFi Using Crowdsensing. IEEE/ACM Trans. Netw. Di Wu, Qiang Liu, Yong Li, Julie A. McCann, Amelia C. Regan, Nalini Venkatasubramanian
2016	—	`conf`	Bootstrap Model Aggregation for Distributed Statistical Learning. NIPS Jun Han, Qiang Liu
2016	J	`jnl`	Bootstrap Model Aggregation for Distributed Statistical Learning. CoRR Jun Han, Qiang Liu
2016	A	`conf`	Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds. UAI Dilin Wang, John W. Fisher III, Qiang Liu
2016	—	`conf`	Learning Infinite RBMs with Frank-Wolfe. NIPS Wei Ping, Qiang Liu, Alexander Ihler
2016	J	`jnl`	Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. CoRR Dilin Wang, Qiang Liu
2016	J	`jnl`	Local Perturb-and-MAP for Structured Prediction. CoRR Gedas Bertasius, Qiang Liu, Lorenzo Torresani, Jianbo Shi
2016	—	`conf`	Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. NIPS Qiang Liu, Dilin Wang
2016	J	`jnl`	Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. CoRR Qiang Liu, Dilin Wang
2015	C	`conf`	Boosting crowdsourcing with expert labels: Local vs. global effects. FUSION Qiang Liu, Alexander Ihler, John W. Fisher III
2015	J	`jnl`	Communication-efficient sparse regression: a one-shot approach. CoRR Jason D. Lee, Yuekai Sun, Qiang Liu, Jonathan E. Taylor
2015	—	`conf`	Decomposition Bounds for Marginal MAP. NIPS Wei Ping, Qiang Liu, Alexander Ihler
2015	J	`jnl`	Decomposition Bounds for Marginal MAP. CoRR Wei Ping, Qiang Liu, Alexander Ihler
2015	A	`conf`	Estimating the Partition Function by Discriminance Sampling. UAI Qiang Liu, Jian Peng, Alexander Ihler, John W. Fisher III
2015	—	`conf`	Probabilistic Variational Bounds for Graphical Models. NIPS Qiang Liu, John W. Fisher III, Alexander Ihler
2015	J	`jnl`	Regularized Minimax Conditional Entropy for Crowdsourcing. CoRR Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah
2014	A*	`conf`	Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. ICML Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek
2014	A	`conf`	CrowdWiFi: efficient crowdsensing of roadside WiFi networks. Middleware Di Wu, Qiang Liu, Yuan Zhang, Julie A. McCann, Amelia Regan, Nalini Venkatasubramanian
2014	—	`conf`	Distributed Estimation, Information Loss and Exponential Families. NIPS Qiang Liu, Alexander Ihler
2014	A*	`conf`	Marginal Structured SVM with Hidden Variables. ICML Wei Ping, Qiang Liu, Alexander Ihler
2014	J	`jnl`	Marginal Structured SVM with Hidden Variables. CoRR Wei Ping, Qiang Liu, Alexander Ihler
2014	—	`—`	Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. Qiang Liu
2013	—	`conf`	Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? NIPS Qiang Liu, Alexander Ihler, Mark Steyvers
2013	J	`jnl`	Variational Algorithms for Marginal MAP CoRR Qiang Liu, Alexander Ihler
2013	—	`conf`	Variational Planning for Graph-based MDPs. NIPS Qiang Cheng, Qiang Liu, Feng Chen, Alexander Ihler
2013	J	`jnl`	Variational algorithms for marginal MAP. J. Mach. Learn. Res. Qiang Liu, Alexander Ihler
2012	J	`jnl`	Belief Propagation for Structured Decision Making CoRR Qiang Liu, Alexander Ihler
2012	A	`conf`	Belief Propagation for Structured Decision Making. UAI Qiang Liu, Alexander Ihler
2012	—	`conf`	Computational Approaches to Sentence Completion. ACL (1) Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J. C. Burges, Ainur Yessenalina, Qiang Liu
2012	A*	`conf`	Distributed Parameter Estimation via Pseudo-likelihood . ICML Qiang Liu, Alexander Ihler
2012	J	`jnl`	Negative Tree Reweighted Belief Propagation CoRR Qiang Liu, Alexander Ihler
2012	J	`jnl`	Variational Algorithms for Marginal MAP CoRR Qiang Liu, Alexander Ihler
2012	—	`conf`	Variational Inference for Crowdsourcing. NIPS Qiang Liu, Jian Peng, Alexander Ihler
2011	A*	`conf`	Bounding the Partition Function using Holder's Inequality. ICML Qiang Liu, Alexander Ihler
2011	A	`conf`	Learning Scale Free Networks by Reweighted L1 regularization. AISTATS Qiang Liu, Alexander Ihler
2011	A	`conf`	Variational Algorithms for Marginal MAP. UAI Qiang Liu, Alexander Ihler
2010	J	`jnl`	Estimating replicate time shifts using Gaussian process regression. Bioinform. Qiang Liu, Kevin K. Lin, Bogi Andersen, Padhraic Smyth, Alexander Ihler
2010	A	`conf`	Learning with Blocks: Composite Likelihood and Contrastive Divergence. AISTATS Arthur U. Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth
2010	A	`conf`	Negative Tree Reweighted Belief Propagation. UAI Qiang Liu, Alexander Ihler
2010	A*	`conf`	Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. ICML Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth
2009	—	`conf`	Improvement of Au cantilever fabrication process with Cu as a sacrificial layer. NEMS Qiang Liu, Bo Liu, Zhihong Li