Qiang Liu

According to our database, Qiang Liu authored at least 144 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity.

Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest N. Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra

CoRR, 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

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

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

CoRR, 2023

InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation.

Xingchao Liu, Xiwen Zhang, Jianzhu Ma, Jian Peng, Qiang Liu

CoRR, 2023

FAMO: Fast Adaptive Multitask Optimization.

Bo Liu, Yihao Feng, Peter Stone, Qiang Liu

CoRR, 2023

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning.

Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, Peter Stone

CoRR, 2023

MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation.

Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Learning Diffusion Bridges on Constrained Domains.

Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow.

Xingchao Liu, Chengyue Gong, Qiang Liu

Proceedings of the Eleventh International Conference on Learning Representations, 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 D. Ellington, Alex Dimakis, Adam R. Klivans

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient Transformer-based 3D Object Detection with Dynamic Token Halting.

Mao Ye, Gregory P. Meyer, Yuning Chai, Qiang Liu

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Noise-Robust Semi-Supervised Learning for Distantly Supervised Relation Extraction.

Xin Sun, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 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

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

FlowGrad: Controlling the Output of Generative ODEs with Gradients.

Xingchao Liu, Lemeng Wu, Shujian Zhang, Chengyue Gong, Wei Ping, Qiang Liu

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning.

Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Human as a Service: Towards Resilient Parking Search System With Sensorless Sensing.

Di Wu, Zhanxiu Zeng, Fengrui Shi, Weiren Yu, Tao Wu, Qiang Liu

IEEE Trans. Intell. Transp. Syst., 2022

PathFusion: Path-consistent Lidar-Camera Deep Feature Fusion.

Lemeng Wu, Dilin Wang, Meng Li, Yunyang Xiong, Raghuraman Krishnamoorthi, Qiang Liu, Vikas Chandra

CoRR, 2022

Neural Volumetric Mesh Generator.

Yan Zheng, Lemeng Wu, Xingchao Liu, Zhen Chen, Qiang Liu, Qixing Huang

CoRR, 2022

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach.

Mao Ye, Bo Liu, Stephen Wright, Peter Stone, Qiang Liu

CoRR, 2022

First Hitting Diffusion Models.

Mao Ye, Lemeng Wu, Qiang Liu

CoRR, 2022

Let us Build Bridges: Understanding and Extending Diffusion Generative Models.

Xingchao Liu, Lemeng Wu, Mao Ye, Qiang Liu

CoRR, 2022

Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning.

Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

CoRR, 2022

Split Localized Conformal Prediction.

Xing Han, Ziyang Tang, Joydeep Ghosh, Qiang Liu

CoRR, 2022

Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning.

Ziyang Tang, Yihao Feng, Qiang Liu

CoRR, 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

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set.

Mao Ye, Qiang Liu

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent.

Ruqi Zhang, Qiang Liu, Xin T. Tong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data.

Mao Ye, Lemeng Wu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Diffusion-based Molecule Generation with Informative Prior Bridges.

Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Harmless Transfer Learning for Item Embeddings.

Chengyue Gong, Xiaocong Du, Dhruv Choudhary, Bhargav Bhushanam, Qiang Liu, Arun Kejariwal

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

A Langevin-like Sampler for Discrete Distributions.

Ruqi Zhang, Xingchao Liu, Qiang Liu

Proceedings of the International Conference on Machine Learning, 2022

Centroid Approximation for Bootstrap: Improving Particle Quality at Inference.

Mao Ye, Qiang Liu

Proceedings of the International Conference on Machine Learning, 2022

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity.

Chengyue Gong, Lemeng Wu, Qiang Liu

Proceedings of the International Conference on Machine Learning, 2022

Energy-Inspired Molecular Conformation Optimization.

Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng

Proceedings of the Tenth International Conference on Learning Representations, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

Continual Learning and Private Unlearning.

Bo Liu, Qiang Liu, Peter Stone

Proceedings of the Conference on Lifelong Learning Agents, 2022

2021

Training Robust Deep Neural Networks via Adversarial Noise Propagation.

Aishan Liu, Xianglong Liu, Hang Yu, Chongzhi Zhang, Qiang Liu, Dacheng Tao

IEEE Trans. Image Process., 2021

FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimization.

Xingchao Liu, Chengyue Gong, Lemeng Wu, Shujian Zhang, Hao Su, Qiang Liu

CoRR, 2021

Centroid Approximation for Bootstrap.

Mao Ye, Qiang Liu

CoRR, 2021

Speeding up Deep Model Training by Sharing Weights and Then Unsharing.

Shuo Yang, Le Hou, Xiaodan Song, Qiang Liu, Denny Zhou

CoRR, 2021

Improve Vision Transformers Training by Suppressing Over-smoothing.

Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

CoRR, 2021

Centroid Transformers: Learning to Abstract with Attention.

Lemeng Wu, Xingchao Liu, Qiang Liu

CoRR, 2021

AlphaNet: Improved Training of Supernet with Alpha-Divergence.

Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

CoRR, 2021

Sampling with Trusthworthy Constraints: A Variational Gradient Framework.

Xingchao Liu, Xin Tong, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent.

Xingchao Liu, Xin Tong, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Conflict-Averse Gradient Descent for Multi-task learning.

Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

argmax centroid.

Chengyue Gong, Mao Ye, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach.

Chengyue Gong, Xingchao Liu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

AlphaNet: Improved Training of Supernets with Alpha-Divergence.

Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra

Proceedings of the 38th International Conference on Machine Learning, 2021

Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition.

Bo Liu, Qiang Liu, Peter Stone, Animesh Garg, Yuke Zhu, Anima Anandkumar

Proceedings of the 38th International Conference on Machine Learning, 2021

VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments.

Lizhen Nie, Mao Ye, Qiang Liu, Dan Nicolae

Proceedings of the 9th International Conference on Learning Representations, 2021

Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds.

Yihao Feng, Ziyang Tang, Na Zhang, Qiang Liu

Proceedings of the 9th International Conference on Learning Representations, 2021

KeepAugment: A Simple Information-Preserving Data Augmentation Approach.

Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence.

Chengyue Gong, Dilin Wang, Qiang Liu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training.

Chengyue Gong, Tongzheng Ren, Mao Ye, Qiang Liu

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision.

Xingchao Liu, Mao Ye, Dengyong Zhou, Qiang Liu

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems.

Di Wu, Lambros Lambrinos, Thomas Przepiorka, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann

IEEE Trans. Intell. Transp. Syst., 2020

Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations.

Qiang Liu, Xin T. Tong

Stat. Comput., 2020

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data.

Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, Zeliang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal

CoRR, 2020

Dimension Independent Generalization Error with Regularized Online Optimization.

Xi Chen, Qiang Liu, Xin T. Tong

CoRR, 2020

Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting.

Lemeng Wu, Mao Ye, Qi Lei, Jason D. Lee, Qiang Liu

CoRR, 2020

Statistical Adaptive Stochastic Gradient Methods.

Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao

CoRR, 2020

MaxUp: A Simple Way to Improve Generalization of Neural Network Training.

ChengYue Gong, Tongzheng Ren, Mao Ye, Qiang Liu

CoRR, 2020

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework.

Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough.

Mao Ye, Lemeng Wu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stein Self-Repulsive Dynamics: Benefits From Past Samples.

Mao Ye, Tongzheng Ren, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks.

Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Implicit Regularization and Convergence for Weight Normalization.

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Off-Policy Interval Estimation with Lipschitz Value Iteration.

Ziyang Tang, Yihao Feng, Na Zhang, Jian Peng, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Certified Monotonic Neural Networks.

Xingchao Liu, Xing Han, Na Zhang, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks.

Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans

Proceedings of the 37th International Conference on Machine Learning, 2020

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection.

Mao Ye, Chengyue Gong, Lizhen Nie, Denny Zhou, Adam R. Klivans, Qiang Liu

Proceedings of the 37th International Conference on Machine Learning, 2020

A Chance-Constrained Generative Framework for Sequence Optimization.

Xianggen Liu, Qiang Liu, Sen Song, Jian Peng

Proceedings of the 37th International Conference on Machine Learning, 2020

Accountable Off-Policy Evaluation With Kernel Bellman Statistics.

Yihao Feng, Tongzheng Ren, Ziyang Tang, Qiang Liu

Proceedings of the 37th International Conference on Machine Learning, 2020

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation.

Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu

Proceedings of the 8th International Conference on Learning Representations, 2020

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning.

Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou

Proceedings of the 8th International Conference on Learning Representations, 2020

Stein Variational Inference for Discrete Distributions.

Jun Han, Fan Ding, Xianglong Liu, Lorenzo Torresani, Jian Peng, Qiang Liu

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions.

Mao Ye, Chengyue Gong, Qiang Liu

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020

2019

ParkCrowd: Reliable Crowdsensing for Aggregation and Dissemination of Parking Space Information.

Fengrui Shi, Di Wu, Dmitri I. Arkhipov, Qiang Liu, Amelia C. Regan, Julie A. McCann

IEEE Trans. Intell. Transp. Syst., 2019

Implicit Regularization of Normalization Methods.

Xiaoxia Wu, Edgar Dobriban, Tongzheng Ren, Shanshan Wu, Zhiyuan Li, Suriya Gunasekar, Rachel A. Ward, Qiang Liu

CoRR, 2019

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent.

Dilin Wang, Meng Li, Lemeng Wu, Vikas Chandra, Qiang Liu

CoRR, 2019

Training Robust Deep Neural Networks via Adversarial Noise Propagation.

Aishan Liu, Xianglong Liu, Chongzhi Zhang, Hang Yu, Qiang Liu, Junfeng He

CoRR, 2019

Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning.

Yang Liu, Yunan Luo, Yuanyi Zhong, Xi Chen, Qiang Liu, Jian Peng

CoRR, 2019

Estimating Zenith Tropospheric Delay Based on GPT2w Model.

Zan Liu, Xihong Chen, Qiang Liu

IEEE Access, 2019

Learning Belief Representations for Imitation Learning in POMDPs.

Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Splitting Steepest Descent for Growing Neural Architectures.

Lemeng Wu, Dilin Wang, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Stein Variational Gradient Descent With Matrix-Valued Kernels.

Dilin Wang, Ziyang Tang, Chandrajit Bajaj, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exploration via Hindsight Goal Generation.

Zhizhou Ren, Kefan Dong, Yuan Zhou, Qiang Liu, Jian Peng

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

A Kernel Loss for Solving the Bellman Equation.

Yihao Feng, Lihong Li, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Improving Neural Language Modeling via Adversarial Training.

Dilin Wang, ChengYue Gong, Qiang Liu

Proceedings of the 36th International Conference on Machine Learning, 2019

Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models.

Dilin Wang, Qiang Liu

Proceedings of the 36th International Conference on Machine Learning, 2019

Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization.

ChengYue Gong, Jian Peng, Qiang Liu

Proceedings of the 36th International Conference on Machine Learning, 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

Proceedings of the 7th International Conference on Learning Representations, 2019

Learning Self-Imitating Diverse Policies.

Tanmay Gangwani, Qiang Liu, Jian Peng

Proceedings of the 7th International Conference on Learning Representations, 2019

Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning.

ChengYue Gong, Zixuan Jiang, Dilin Wang, Yibo Lin, Qiang Liu, David Z. Pan

Proceedings of the International Conference on Computer-Aided Design, 2019

LithoROC: lithography hotspot detection with explicit ROC optimization.

Wei Ye, Yibo Lin, Meng Li, Qiang Liu, David Z. Pan

Proceedings of the 24th Asia and South Pacific Design Automation Conference, 2019

2018

Learning to Explore with Meta-Policy Gradient.

Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

CoRR, 2018

Variational Inference with Tail-adaptive f-Divergence.

Dilin Wang, Hao Liu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Stein Variational Gradient Descent as Moment Matching.

Qiang Liu, Dilin Wang

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation.

Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Energy-efficient Amortized Inference with Cascaded Deep Classifiers.

Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Efficient Localized Inference for Large Graphical Models.

Jinglin Chen, Jian Peng, Qiang Liu

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Learning to Explore via Meta-Policy Gradient.

Tianbing Xu, Qiang Liu, Liang Zhao, Jian Peng

Proceedings of the 35th International Conference on Machine Learning, 2018

Stein Variational Message Passing for Continuous Graphical Models.

Dilin Wang, Zhe Zeng, Qiang Liu

Proceedings of the 35th International Conference on Machine Learning, 2018

Stein Variational Gradient Descent Without Gradient.

Jun Han, Qiang Liu

Proceedings of the 35th International Conference on Machine Learning, 2018

On the Discrimination-Generalization Tradeoff in GANs.

Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He

Proceedings of the 6th International Conference on Learning Representations, 2018

An Optimization View on Dynamic Routing Between Capsules.

Dilin Wang, Qiang Liu

Proceedings of the 6th International Conference on Learning Representations, 2018

Action-dependent Control Variates for Policy Optimization via Stein Identity.

Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

From Intermittent to Ubiquitous: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization.

Di Wu, Dmitri I. Arkhipov, Thomas Przepiorka, Yong Li, Bin Guo, Qiang Liu

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017

Structured Stein Variational Inference for Continuous Graphical Models.

Dilin Wang, Zhe Zeng, Qiang Liu

CoRR, 2017

Sample-efficient Policy Optimization with Stein Control Variate.

Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu

CoRR, 2017

Stochastic Variance Reduction for Policy Gradient Estimation.

Tianbing Xu, Qiang Liu, Jian Peng

CoRR, 2017

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE.

Qiang Liu, Dilin Wang

CoRR, 2017

Stein Variational Policy Gradient.

Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Stein Variational Adaptive Importance Sampling.

Jun Han, Qiang Liu

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

Learning to Draw Samples with Amortized Stein Variational Gradient Descent.

Yihao Feng, Dilin Wang, Qiang Liu

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 2017

2016

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning.

Dilin Wang, Qiang Liu

CoRR, 2016

Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds.

Dilin Wang, John W. Fisher III, Qiang Liu

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Learning Infinite RBMs with Frank-Wolfe.

Wei Ping, Qiang Liu, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm.

Qiang Liu, Dilin Wang

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Bootstrap Model Aggregation for Distributed Statistical Learning.

Jun Han, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Regularized Minimax Conditional Entropy for Crowdsourcing.

Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek, Nihar B. Shah

CoRR, 2015

Estimating the Partition Function by Discriminance Sampling.

Qiang Liu, Jian Peng, Alexander Ihler, John W. Fisher III

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Decomposition Bounds for Marginal MAP.

Wei Ping, Qiang Liu, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Probabilistic Variational Bounds for Graphical Models.

Qiang Liu, John W. Fisher III, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Boosting crowdsourcing with expert labels: Local vs. global effects.

Qiang Liu, Alexander Ihler, John W. Fisher III

Proceedings of the 18th International Conference on Information Fusion, 2015

2014

Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework.

Qiang Liu

PhD thesis, 2014

Distributed Estimation, Information Loss and Exponential Families.

Qiang Liu, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy.

Dengyong Zhou, Qiang Liu, John C. Platt, Christopher Meek

Proceedings of the 31th International Conference on Machine Learning, 2014

Marginal Structured SVM with Hidden Variables.

Wei Ping, Qiang Liu, Alexander Ihler

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Variational algorithms for marginal MAP.

Qiang Liu, Alexander Ihler

J. Mach. Learn. Res., 2013

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?

Qiang Liu, Alexander Ihler, Mark Steyvers

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Variational Planning for Graph-based MDPs.

Qiang Cheng, Qiang Liu, Feng Chen, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012

Belief Propagation for Structured Decision Making.

Qiang Liu, Alexander Ihler

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Variational Inference for Crowdsourcing.

Qiang Liu, Jian Peng, Alexander Ihler

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Distributed Parameter Estimation via Pseudo-likelihood .

Qiang Liu, Alexander Ihler

Proceedings of the 29th International Conference on Machine Learning, 2012

Computational Approaches to Sentence Completion.

Geoffrey Zweig, John C. Platt, Christopher Meek, Christopher J. C. Burges, Ainur Yessenalina, Qiang Liu

Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, July 8-14, 2012, Jeju Island, Korea, 2012

2011

Learning Scale Free Networks by Reweighted L1 regularization.

Qiang Liu, Alexander Ihler

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Bounding the Partition Function using Holder's Inequality.

Qiang Liu, Alexander Ihler

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

Learning with Blocks: Composite Likelihood and Contrastive Divergence.

Arthur U. Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Estimating replicate time shifts using Gaussian process regression.

Qiang Liu, Kevin K. Lin, Bogi Andersen, Padhraic Smyth, Alexander Ihler

Bioinform., 2010

Negative Tree Reweighted Belief Propagation.

Qiang Liu, Alexander Ihler

Proceedings of the UAI 2010, 2010

Particle Filtered MCMC-MLE with Connections to Contrastive Divergence.

Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic Smyth

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010