Improvement of Au cantilever fabrication process with Cu as a sacrificial layer. - Qiang Liu 001; Bo Liu 042; Zhihong Li (NEMS, 2009)

Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. - Arthur U. Asuncion; Qiang Liu 001; Alexander T. Ihler; Padhraic Smyth (ICML, 2010)

Negative Tree Reweighted Belief Propagation. - Qiang Liu 001; Alexander Ihler (UAI, 2010)

Estimating replicate time shifts using Gaussian process regression. - Qiang Liu 001; Kevin K. Lin; Bogi Andersen; Padhraic Smyth; Alexander Ihler (Bioinform., 2010)

Learning with Blocks: Composite Likelihood and Contrastive Divergence. - Arthur U. Asuncion; Qiang Liu 001; Alexander Ihler; Padhraic Smyth (AISTATS, 2010)

Bounding the Partition Function using Holder's Inequality. - Qiang Liu 001; Alexander Ihler (ICML, 2011)

Learning Scale Free Networks by Reweighted L1 regularization. - Qiang Liu 001; Alexander Ihler (AISTATS, 2011)

Computational Approaches to Sentence Completion. - Geoffrey Zweig; John C. Platt; Christopher Meek; Christopher J. C. Burges; Ainur Yessenalina; Qiang Liu 001 (ACL (1), 2012)

Distributed Parameter Estimation via Pseudo-likelihood . - Qiang Liu 001; Alexander Ihler (ICML, 2012)

Variational Inference for Crowdsourcing. - Qiang Liu 001; Jian Peng 001; Alexander Ihler (NIPS, 2012)

Belief Propagation for Structured Decision Making. - Qiang Liu 001; Alexander Ihler (UAI, 2012)

Variational Planning for Graph-based MDPs. - Qiang Cheng; Qiang Liu 001; Feng Chen 007; Alexander Ihler (NIPS, 2013)

Scoring Workers in Crowdsourcing: How Many Control Questions are Enough? - Qiang Liu 001; Alexander Ihler; Mark Steyvers (NIPS, 2013)

Variational algorithms for marginal MAP. - Qiang Liu 001; Alexander Ihler (J. Mach. Learn. Res., 2013)

Marginal Structured SVM with Hidden Variables. - Wei Ping; Qiang Liu 001; Alexander Ihler (ICML, 2014)

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy. - Dengyong Zhou; Qiang Liu 001; John C. Platt; Christopher Meek (ICML, 2014)

CrowdWiFi: efficient crowdsensing of roadside WiFi networks. - Di Wu 002; Qiang Liu 001; Yuan Zhang; Julie A. McCann; Amelia Regan; Nalini Venkatasubramanian (Middleware, 2014)

Distributed Estimation, Information Loss and Exponential Families. - Qiang Liu 001; Alexander Ihler (NIPS, 2014)

Reasoning and Decisions in Probabilistic Graphical Models - A Unified Framework. - Qiang Liu 001 (2014)

Boosting crowdsourcing with expert labels: Local vs. global effects. - Qiang Liu 001; Alexander Ihler; John W. Fisher III (FUSION, 2015)

Probabilistic Variational Bounds for Graphical Models. - Qiang Liu 001; John W. Fisher III; Alexander Ihler (NIPS, 2015)

Decomposition Bounds for Marginal MAP. - Wei Ping; Qiang Liu 001; Alexander Ihler (NIPS, 2015)

Estimating the Partition Function by Discriminance Sampling. - Qiang Liu 001; Jian Peng 001; Alexander Ihler; John W. Fisher III (UAI, 2015)

Communication-efficient sparse regression: a one-shot approach. - Jason D. Lee; Yuekai Sun; Qiang Liu 001; Jonathan E. Taylor (CoRR, 2015)

Regularized Minimax Conditional Entropy for Crowdsourcing. - Dengyong Zhou; Qiang Liu 001; John C. Platt; Christopher Meek; Nihar B. Shah (CoRR, 2015)

A Kernelized Stein Discrepancy for Goodness-of-fit Tests. - Qiang Liu 001; Jason D. Lee; Michael I. Jordan (ICML, 2016)

Bootstrap Model Aggregation for Distributed Statistical Learning. - Jun Han 002; Qiang Liu 001 (NIPS, 2016)

Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm. - Qiang Liu 001; Dilin Wang (NIPS, 2016)

Learning Infinite RBMs with Frank-Wolfe. - Wei Ping; Qiang Liu 001; Alexander Ihler (NIPS, 2016)

Efficient Observation Selection in Probabilistic Graphical Models Using Bayesian Lower Bounds. - Dilin Wang; John W. Fisher III; Qiang Liu 001 (UAI, 2016)

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning. - Dilin Wang; Qiang Liu 001 (CoRR, 2016)

Adaptive Lookup of Open WiFi Using Crowdsensing. - Di Wu 002; Qiang Liu 001; Yong Li 008; Julie A. McCann; Amelia C. Regan; Nalini Venkatasubramanian (IEEE/ACM Trans. Netw., 2016)

Local Perturb-and-MAP for Structured Prediction. - Gedas Bertasius; Qiang Liu 001; Lorenzo Torresani; Jianbo Shi (AISTATS, 2017)

Black-box Importance Sampling. - Qiang Liu 001; Jason D. Lee (AISTATS, 2017)

DeepOpp: Context-Aware Mobile Access to Social Media Content on Underground Metro Systems. - Di Wu 002; Dmitri I. Arkhipov; Thomas Przepiorka; Qiang Liu 001; Julie A. McCann; Amelia C. Regan (ICDCS, 2017)

Learning to Draw Samples with Amortized Stein Variational Gradient Descent. - Yihao Feng; Dilin Wang; Qiang Liu 001 (UAI, 2017)

Stein Variational Adaptive Importance Sampling. - Jun Han 002; Qiang Liu 001 (UAI, 2017)

Stein Variational Policy Gradient. - Yang Liu 097; Prajit Ramachandran; Qiang Liu 001; Jian Peng 001 (UAI, 2017)

Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE. - Qiang Liu 001; Dilin Wang (CoRR, 2017)

Stochastic Variance Reduction for Policy Gradient Estimation. - Tianbing Xu; Qiang Liu 001; Jian Peng 001 (CoRR, 2017)

Sample-efficient Policy Optimization with Stein Control Variate. - Hao Liu; Yihao Feng; Yi Mao; Dengyong Zhou; Jian Peng 001; Qiang Liu 001 (CoRR, 2017)

Structured Stein Variational Inference for Continuous Graphical Models. - Dilin Wang; Zhe Zeng; Qiang Liu 001 (CoRR, 2017)

From Intermittent to Ubiquitous: Enhancing Mobile Access to Online Social Networks with Opportunistic Optimization. - Di Wu 002; Dmitri I. Arkhipov; Thomas Przepiorka; Yong Li 008; Bin Guo 001; Qiang Liu 001 (Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2017)

Communication-efficient Sparse Regression. - Jason D. Lee; Qiang Liu 001; Yuekai Sun; Jonathan E. Taylor (J. Mach. Learn. Res., 2017)

Action-dependent Control Variates for Policy Optimization via Stein Identity. - Hao Liu; Yihao Feng; Yi Mao; Dengyong Zhou; Jian Peng 001; Qiang Liu 001 (ICLR (Poster), 2018)

An Optimization View on Dynamic Routing Between Capsules. - Dilin Wang; Qiang Liu 001 (ICLR (Workshop), 2018)

On the Discrimination-Generalization Tradeoff in GANs. - Pengchuan Zhang; Qiang Liu 001; Dengyong Zhou; Tao Xu; Xiaodong He 001 (ICLR (Poster), 2018)

Stein Variational Gradient Descent Without Gradient. - Jun Han 002; Qiang Liu 001 (ICML, 2018)

Stein Variational Message Passing for Continuous Graphical Models. - Dilin Wang; Zhe Zeng; Qiang Liu 001 (ICML, 2018)

Learning to Explore via Meta-Policy Gradient. - Tianbing Xu; Qiang Liu 001; Liang Zhao 006; Jian Peng 001 (ICML, 2018)

Efficient Localized Inference for Large Graphical Models. - Jinglin Chen; Jian Peng 001; Qiang Liu 001 (IJCAI, 2018)

Energy-efficient Amortized Inference with Cascaded Deep Classifiers. - Jiaqi Guan; Yang Liu 097; Qiang Liu 001; Jian Peng 001 (IJCAI, 2018)

Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. - Qiang Liu 001; Lihong Li 001; Ziyang Tang; Dengyong Zhou (NeurIPS, 2018)

Stein Variational Gradient Descent as Moment Matching. - Qiang Liu 001; Dilin Wang (NeurIPS, 2018)

Variational Inference with Tail-adaptive f-Divergence. - Dilin Wang; Hao Liu; Qiang Liu 001 (NeurIPS, 2018)

Learning to Explore with Meta-Policy Gradient. - Tianbing Xu; Qiang Liu 001; Liang Zhao 006; Jian Peng 001 (CoRR, 2018)

On the Margin Theory of Feedforward Neural Networks. - Colin Wei; Jason D. Lee; Qiang Liu 001; Tengyu Ma 001 (CoRR, 2018)

LithoROC: lithography hotspot detection with explicit ROC optimization. - Wei Ye 008; Yibo Lin; Meng Li 004; Qiang Liu 001; David Z. Pan (ASP-DAC, 2019)

Mixed Precision Neural Architecture Search for Energy Efficient Deep Learning. - ChengYue Gong; Zixuan Jiang; Dilin Wang; Yibo Lin; Qiang Liu 001; David Z. Pan (ICCAD, 2019)

Learning Self-Imitating Diverse Policies. - Tanmay Gangwani; Qiang Liu 001; Jian Peng 001 (ICLR (Poster), 2019)

Off-Policy Evaluation and Learning from Logged Bandit Feedback: Error Reduction via Surrogate Policy. - Yuan Xie 005; Boyi Liu 001; Qiang Liu 001; Zhaoran Wang 001; Yuan Zhou 007; Jian Peng 001 (ICLR (Poster), 2019)

Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization. - ChengYue Gong; Jian Peng 001; Qiang Liu 001 (ICML, 2019)

Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models. - Dilin Wang; Qiang Liu 001 (ICML, 2019)

Improving Neural Language Modeling via Adversarial Training. - Dilin Wang; ChengYue Gong; Qiang Liu 001 (ICML, 2019)

A Kernel Loss for Solving the Bellman Equation. - Yihao Feng; Lihong Li 001; Qiang Liu 001 (NeurIPS, 2019)

Exploration via Hindsight Goal Generation. - Zhizhou Ren; Kefan Dong; Yuan Zhou 007; Qiang Liu 001; Jian Peng 001 (NeurIPS, 2019)

Stein Variational Gradient Descent With Matrix-Valued Kernels. - Dilin Wang; Ziyang Tang; Chandrajit Bajaj; Qiang Liu 001 (NeurIPS, 2019)

Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel. - Colin Wei; Jason D. Lee; Qiang Liu 001; Tengyu Ma 001 (NeurIPS, 2019)

Splitting Steepest Descent for Growing Neural Architectures. - Lemeng Wu; Dilin Wang; Qiang Liu 001 (NeurIPS, 2019)

Learning Belief Representations for Imitation Learning in POMDPs. - Tanmay Gangwani; Joel Lehman; Qiang Liu 001; Jian Peng 001 (UAI, 2019)

Estimating Zenith Tropospheric Delay Based on GPT2w Model. - Zan Liu; Xihong Chen; Qiang Liu 001 (IEEE Access, 2019)

Sequence Modeling of Temporal Credit Assignment for Episodic Reinforcement Learning. - Yang Liu 097; Yunan Luo; Yuanyi Zhong; Xi Chen; Qiang Liu 001; Jian Peng 001 (CoRR, 2019)

Training Robust Deep Neural Networks via Adversarial Noise Propagation. - Aishan Liu; Xianglong Liu 001; Chongzhi Zhang; Hang Yu 016; Qiang Liu 001; Junfeng He (CoRR, 2019)

Energy-Aware Neural Architecture Optimization with Fast Splitting Steepest Descent. - Dilin Wang; Meng Li 004; Lemeng Wu; Vikas Chandra; Qiang Liu 001 (CoRR, 2019)

Implicit Regularization of Normalization Methods. - Xiaoxia Wu; Edgar Dobriban; Tongzheng Ren; Shanshan Wu; Zhiyuan Li; Suriya Gunasekar; Rachel A. Ward; Qiang Liu 001 (CoRR, 2019)

ParkCrowd: Reliable Crowdsensing for Aggregation and Dissemination of Parking Space Information. - Fengrui Shi; Di Wu 002; Dmitri I. Arkhipov; Qiang Liu 001; Amelia C. Regan; Julie A. McCann (IEEE Trans. Intell. Transp. Syst., 2019)

SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions. - Mao Ye 006; Chengyue Gong; Qiang Liu 001 (ACL, 2020)

Stein Variational Inference for Discrete Distributions. - Jun Han 002; Fan Ding; Xianglong Liu 001; Lorenzo Torresani; Jian Peng 001; Qiang Liu 001 (AISTATS, 2020)

Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. - Ali Mousavi 003; Lihong Li 001; Qiang Liu 001; Denny Zhou (ICLR, 2020)

Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. - Ziyang Tang; Yihao Feng; Lihong Li 001; Dengyong Zhou; Qiang Liu 001 (ICLR, 2020)

Accountable Off-Policy Evaluation With Kernel Bellman Statistics. - Yihao Feng; Tongzheng Ren; Ziyang Tang; Qiang Liu 001 (ICML, 2020)

A Chance-Constrained Generative Framework for Sequence Optimization. - Xianggen Liu; Qiang Liu 001; Sen Song; Jian Peng 001 (ICML, 2020)

Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection. - Mao Ye 006; Chengyue Gong; Lizhen Nie; Denny Zhou; Adam R. Klivans; Qiang Liu 001 (ICML, 2020)

Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. - Denny Zhou; Mao Ye 006; Chen Chen; Tianjian Meng; Mingxing Tan; Xiaodan Song; Quoc V. Le; Qiang Liu 001; Dale Schuurmans (ICML, 2020)

Certified Monotonic Neural Networks. - Xingchao Liu; Xing Han; Na Zhang; Qiang Liu 001 (NeurIPS, 2020)

Off-Policy Interval Estimation with Lipschitz Value Iteration. - Ziyang Tang; Yihao Feng; Na Zhang; Jian Peng 001; Qiang Liu 001 (NeurIPS, 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 001 (NeurIPS, 2020)

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks. - Lemeng Wu; Bo Liu 042; Peter Stone 001; Qiang Liu 001 (NeurIPS, 2020)

Stein Self-Repulsive Dynamics: Benefits From Past Samples. - Mao Ye 006; Tongzheng Ren; Qiang Liu 001 (NeurIPS, 2020)

Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough. - Mao Ye 006; Lemeng Wu; Qiang Liu 001 (NeurIPS, 2020)

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. - Dinghuai Zhang; Mao Ye 006; Chengyue Gong; Zhanxing Zhu; Qiang Liu 001 (NeurIPS, 2020)

MaxUp: A Simple Way to Improve Generalization of Neural Network Training. - ChengYue Gong; Tongzheng Ren; Mao Ye 006; Qiang Liu 001 (CoRR, 2020)

Statistical Adaptive Stochastic Gradient Methods. - Pengchuan Zhang; Hunter Lang; Qiang Liu 001; Lin Xiao (CoRR, 2020)

Steepest Descent Neural Architecture Optimization: Escaping Local Optimum with Signed Neural Splitting. - Lemeng Wu; Mao Ye 006; Qi Lei; Jason D. Lee; Qiang Liu 001 (CoRR, 2020)

Dimension Independent Generalization Error with Regularized Online Optimization. - Xi Chen; Qiang Liu 001; Xin T. Tong (CoRR, 2020)

Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data. - Mao Ye 006; Dhruv Choudhary; Jiecao Yu; Ellie Wen; Zeliang Chen; Jiyan Yang; Jongsoo Park; Qiang Liu 001; Arun Kejariwal (CoRR, 2020)

Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equations. - Qiang Liu 001; Xin T. Tong (Stat. Comput., 2020)

Enabling Efficient Offline Mobile Access to Online Social Media on Urban Underground Metro Systems. - Di Wu 002; Lambros Lambrinos; Thomas Przepiorka; Dmitri I. Arkhipov; Qiang Liu 001; Amelia C. Regan; Julie A. McCann (IEEE Trans. Intell. Transp. Syst., 2020)

Post-training Quantization with Multiple Points: Mixed Precision without Mixed Precision. - Xingchao Liu; Mao Ye 006; Dengyong Zhou; Qiang Liu 001 (AAAI, 2021)

MaxUp: Lightweight Adversarial Training With Data Augmentation Improves Neural Network Training. - Chengyue Gong; Tongzheng Ren; Mao Ye 006; Qiang Liu 001 (CVPR, 2021)

AlphaMatch: Improving Consistency for Semi-Supervised Learning With Alpha-Divergence. - Chengyue Gong; Dilin Wang; Qiang Liu 001 (CVPR, 2021)

KeepAugment: A Simple Information-Preserving Data Augmentation Approach. - Chengyue Gong; Dilin Wang; Meng Li 004; Vikas Chandra; Qiang Liu 001 (CVPR, 2021)

Non-asymptotic Confidence Intervals of Off-policy Evaluation: Primal and Dual Bounds. - Yihao Feng; Ziyang Tang; Na Zhang; Qiang Liu 001 (ICLR, 2021)

VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. - Lizhen Nie; Mao Ye 006; Qiang Liu 001; Dan Nicolae (ICLR, 2021)

Coach-Player Multi-agent Reinforcement Learning for Dynamic Team Composition. - Bo Liu 042; Qiang Liu 001; Peter Stone 001; Animesh Garg; Yuke Zhu; Anima Anandkumar (ICML, 2021)

AlphaNet: Improved Training of Supernets with Alpha-Divergence. - Dilin Wang; Chengyue Gong; Meng Li 004; Qiang Liu 001; Vikas Chandra (ICML, 2021)

Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach. - Chengyue Gong; Xingchao Liu; Qiang Liu 001 (NeurIPS, 2021)

argmax centroid. - Chengyue Gong; Mao Ye 006; Qiang Liu 001 (NeurIPS, 2021)

Conflict-Averse Gradient Descent for Multi-task learning. - Bo Liu 042; Xingchao Liu; Xiaojie Jin; Peter Stone 001; Qiang Liu 001 (NeurIPS, 2021)

Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent. - Xingchao Liu; Xin Tong; Qiang Liu 001 (NeurIPS, 2021)

Sampling with Trusthworthy Constraints: A Variational Gradient Framework. - Xingchao Liu; Xin Tong; Qiang Liu 001 (NeurIPS, 2021)

AlphaNet: Improved Training of Supernet with Alpha-Divergence. - Dilin Wang; Chengyue Gong; Meng Li 004; Qiang Liu 001; Vikas Chandra (CoRR, 2021)

Centroid Transformers: Learning to Abstract with Attention. - Lemeng Wu; Xingchao Liu; Qiang Liu 001 (CoRR, 2021)

Improve Vision Transformers Training by Suppressing Over-smoothing. - Chengyue Gong; Dilin Wang; Meng Li 004; Vikas Chandra; Qiang Liu 001 (CoRR, 2021)

Speeding up Deep Model Training by Sharing Weights and Then Unsharing. - Shuo Yang; Le Hou; Xiaodan Song; Qiang Liu 001; Denny Zhou (CoRR, 2021)

Centroid Approximation for Bootstrap. - Mao Ye 006; Qiang Liu 001 (CoRR, 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 001 (CoRR, 2021)

Explainable deep learning for efficient and robust pattern recognition: A survey of recent developments. - Xiao Bai 001; Xiang Wang 014; Xianglong Liu 001; Qiang Liu 001; Jingkuan Song; Nicu Sebe; Been Kim (Pattern Recognit., 2021)

Training Robust Deep Neural Networks via Adversarial Noise Propagation. - Aishan Liu; Xianglong Liu 001; Hang Yu 016; Chongzhi Zhang; Qiang Liu 001; Dacheng Tao (IEEE Trans. Image Process., 2021)

Continual Learning and Private Unlearning. - Bo Liu 042; Qiang Liu 001; Peter Stone 001 (CoLLAs, 2022)

NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training. - Chengyue Gong; Dilin Wang; Meng Li 004; Xinlei Chen; Zhicheng Yan 001; Yuandong Tian; Qiang Liu 001; Vikas Chandra (ICLR, 2022)

Energy-Inspired Molecular Conformation Optimization. - Jiaqi Guan; Wesley Wei Qian; Qiang Liu 001; Wei-Ying Ma; Jianzhu Ma; Jian Peng 001 (ICLR, 2022)

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity. - Chengyue Gong; Lemeng Wu; Qiang Liu 001 (ICML, 2022)

Centroid Approximation for Bootstrap: Improving Particle Quality at Inference. - Mao Ye 006; Qiang Liu 001 (ICML, 2022)

A Langevin-like Sampler for Discrete Distributions. - Ruqi Zhang; Xingchao Liu; Qiang Liu 001 (ICML, 2022)

Harmless Transfer Learning for Item Embeddings. - Chengyue Gong; Xiaocong Du; Dhruv Choudhary; Bhargav Bhushanam; Qiang Liu 001; Arun Kejariwal (NAACL-HLT (Findings), 2022)

BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach. - Bo Liu 042; Mao Ye 006; Stephen Wright; Peter Stone 001; Qiang Liu 001 (NeurIPS, 2022)

Diffusion-based Molecule Generation with Informative Prior Bridges. - Lemeng Wu; Chengyue Gong; Xingchao Liu; Mao Ye 006; Qiang Liu 001 (NeurIPS, 2022)

First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data. - Mao Ye 006; Lemeng Wu; Qiang Liu 001 (NeurIPS, 2022)

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent. - Ruqi Zhang; Qiang Liu 001; Xin T. Tong (NeurIPS, 2022)

Pareto navigation gradient descent: a first-order algorithm for optimization in pareto set. - Mao Ye 006; Qiang Liu 001 (UAI, 2022)

Future gradient descent for adapting the temporal shifting data distribution in online recommendation systems. - Mao Ye 006; Ruichen Jiang; Haoxiang Wang 003; Dhruv Choudhary; Xiaocong Du; Bhargav Bhushanam; Aryan Mokhtari; Arun Kejariwal; Qiang Liu 001 (UAI, 2022)

Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning. - Ziyang Tang; Yihao Feng; Qiang Liu 001 (CoRR, 2022)

Split Localized Conformal Prediction. - Xing Han; Ziyang Tang; Joydeep Ghosh; Qiang Liu 001 (CoRR, 2022)

Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning. - Bo Liu 042; Yihao Feng; Qiang Liu 001; Peter Stone 001 (CoRR, 2022)

Let us Build Bridges: Understanding and Extending Diffusion Generative Models. - Xingchao Liu; Lemeng Wu; Mao Ye 006; Qiang Liu 001 (CoRR, 2022)

First Hitting Diffusion Models. - Mao Ye 006; Lemeng Wu; Qiang Liu 001 (CoRR, 2022)

Neural Volumetric Mesh Generator. - Yan Zheng; Lemeng Wu; Xingchao Liu; Zhen Chen; Qiang Liu 001; Qixing Huang (CoRR, 2022)

Human as a Service: Towards Resilient Parking Search System With Sensorless Sensing. - Di Wu 002; Zhanxiu Zeng; Fengrui Shi; Weiren Yu; Tao Wu 005; Qiang Liu 001 (IEEE Trans. Intell. Transp. Syst., 2022)

Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning. - Bo Liu 042; Yihao Feng; Qiang Liu 001; Peter Stone 001 (AAAI, 2023)

FlowGrad: Controlling the Output of Generative ODEs with Gradients. - Xingchao Liu; Lemeng Wu; Shujian Zhang; Chengyue Gong; Wei Ping; Qiang Liu 001 (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 001 (CVPR, 2023)

Efficient Transformer-based 3D Object Detection with Dynamic Token Halting. - Mao Ye 006; Gregory P. Meyer; Yuning Chai; Qiang Liu 001 (ICCV, 2023)

HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing. - Tianlong Chen 001; Chengyue Gong; Daniel Jesus Diaz; Xuxi Chen; Jordan Tyler Wells; Qiang Liu 001; Zhangyang Wang; Andrew D. Ellington; Alex Dimakis; Adam R. Klivans (ICLR, 2023)

Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. - Xingchao Liu; Chengyue Gong; Qiang Liu 001 (ICLR, 2023)

Learning Diffusion Bridges on Constrained Domains. - Xingchao Liu; Lemeng Wu; Mao Ye 006; Qiang Liu 001 (ICLR, 2023)

MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. - Xingang Peng; Jiaqi Guan; Qiang Liu 001; Jianzhu Ma (ICML, 2023)

FAMO: Fast Adaptive Multitask Optimization. - Bo Liu 042; Yihao Feng; Peter Stone 001; Qiang Liu 001 (NeurIPS, 2023)

LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning. - Bo Liu 042; Yifeng Zhu; Chongkai Gao; Yihao Feng; Qiang Liu 001; Yuke Zhu; Peter Stone 001 (NeurIPS, 2023)

LLM+P: Empowering Large Language Models with Optimal Planning Proficiency. - Bo Liu 042; Yuqian Jiang; Xiaohan Zhang 002; Qiang Liu 001; Shiqi Zhang 001; Joydeep Biswas; Peter Stone 001 (CoRR, 2023)

InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation. - Xingchao Liu; Xiwen Zhang; Jianzhu Ma; Jian Peng 007; Qiang Liu 001 (CoRR, 2023)

PathFusion: Path-Consistent Lidar-Camera Deep Feature Fusion. - Lemeng Wu; Dilin Wang; Meng Li 004; Yunyang Xiong; Raghuraman Krishnamoorthi; Qiang Liu 001; Vikas Chandra (3DV, 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 001 (AAAI, 2024)

Taming Mode Collapse in Score Distillation for Text-to-3D Generation. - Peihao Wang; Dejia Xu; Zhiwen Fan; Dilin Wang; Sreyas Mohan; Forrest N. Iandola; Rakesh Ranjan; Yilei Li; Qiang Liu 001; Zhangyang Wang; Vikas Chandra (CVPR, 2024)

SlimFlow: Training Smaller One-Step Diffusion Models with Rectified Flow. - Yuanzhi Zhu; Xingchao Liu; Qiang Liu 001 (ECCV (82), 2024)

Lion Secretly Solves a Constrained Optimization: As Lyapunov Predicts. - Lizhang Chen; Bo Liu 042; Kaizhao Liang; Qiang Liu 001 (ICLR, 2024)

InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation. - Xingchao Liu; Xiwen Zhang; Jianzhu Ma; Jian Peng 001; Qiang Liu 001 (ICLR, 2024)

Evolution-Inspired Loss Functions for Protein Representation Learning. - Chengyue Gong; Adam R. Klivans; James Loy; Tianlong Chen 001; Qiang Liu 001; 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 001; Yunan Luo; Jian Peng 001 (ICML, 2024)

AdaFlow: Imitation Learning with Variance-Adaptive Flow-Based Policies. - Xixi Hu 001; Qiang Liu 001; Xingchao Liu; Bo Liu 042 (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 001; Jian Peng 001; Jianzhu Ma (NeurIPS, 2024)

Memory-Efficient LLM Training with Online Subspace Descent. - Kaizhao Liang; Bo Liu 042; Lizhang Chen; Qiang Liu 001 (NeurIPS, 2024)

Communication Efficient Distributed Training with Distributed Lion. - Bo Liu 042; Lemeng Wu; Lizhang Chen; Kaizhao Liang; Jiaxu Zhu; Chen Liang; Raghuraman Krishnamoorthi; Qiang Liu 001 (NeurIPS, 2024)

PeRFlow: Piecewise Rectified Flow as Universal Plug-and-Play Accelerator. - Hanshu Yan; Xingchao Liu; Jiachun Pan; Jun Hao Liew; Qiang Liu 001; Jiashi Feng (NeurIPS, 2024)

H-Fac: Memory-Efficient Optimization with Factorized Hamiltonian Descent. - Son Nguyen; Lizhang Chen; Bo Liu 042; Qiang Liu 001 (CoRR, 2024)

On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability. - Kevin Wang; Junbo Li; Neel P. Bhatt; Yihan Xi; Qiang Liu 001; Ufuk Topcu; Zhangyang Wang (CoRR, 2024)

Fine-Grained Gradient Restriction: A Simple Approach for Mitigating Catastrophic Forgetting. - Bo Liu 042; Mao Ye 006; Peter Stone 001; Qiang Liu 001 (CoRR, 2024)

Cautious Optimizers: Improving Training with One Line of Code. - Kaizhao Liang; Lizhang Chen; Bo Liu 042; Qiang Liu 001 (CoRR, 2024)

Rectified Flow For Structure Based Drug Design. - Daiheng Zhang; Chengyue Gong; Qiang Liu 001 (CoRR, 2024)

SKIM: Any-bit Quantization Pushing The Limits of Post-Training Quantization. - Runsheng Bai; Bo Liu 042; Qiang Liu 001 (CoRR, 2024)

Memory-Efficient Optimization with Factorized Hamiltonian Descent. - Son Nguyen; Lizhang Chen; Bo Liu 042; Qiang Liu 001 (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 N. Iandola; Rakesh Ranjan; Yilei Li; Qiang Liu 001; Zhangyang Wang; Vikas Chandra (AISTATS, 2025)

AMO Sampler: Enhancing Text Rendering with Overshooting. - Xixi Hu 001; Keyang Xu; Bo Liu 042; Qiang Liu 001; Hongliang Fei (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 001; Rakesh Ranjan (CVPR, 2025)

Longhorn: State Space Models are Amortized Online Learners. - Bo Liu 042; Rui Wang; Lemeng Wu; Yihao Feng; Peter Stone 001; Qiang Liu 001 (ICLR, 2025)

PIPA: Preference Alignment as Prior-Informed Statistical Estimation. - Junbo Li; Zhangyang Wang; Qiang Liu 001 (CoRR, 2025)

Improving Adaptive Moment Optimization via Preconditioner Diagonalization. - Son Nguyen; Bo Liu 042; Lizhang Chen; Qiang Liu 001 (CoRR, 2025)

Improving Rectified Flow with Boundary Conditions. - Xixi Hu 001; Runlong Liao; Keyang Xu; Bo Liu 042; Yeqing Li; Eugene Ie; Hongliang Fei; Qiang Liu 001 (CoRR, 2025)