| 2026 | ACL | Gold-Medal-Level Olympiad Geometry Solving with Efficient Heuristic Auxiliary Constructions. | Boyan Duan, Xiao Liang, Shuai Lu, Yaoxiang Wang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Mao Yang, Weizhu Chen, Yeyun Gong |
| 2026 | ACL | Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models. | Eric Hanchen Jiang, Levina Li, Frank Wan, Xiao Liang, Sophia Yin, Yuchen Wu, Xinfeng Li, Yizhou Sun, Wei Wang, Kai-Wei Chang, Ying Nian Wu |
| 2026 | ACL | Mitigating Over-Refusal in Aligned Large Language Models via Inference-Time Activation Energy. | Eric Hanchen Jiang, Weixuan Ou, Run Liu, Shengyuan Pang, Guancheng Wan, Ranjie Duan, Wei Dong, Kai-Wei Chang, Xiaofeng Wang, Ying Nian Wu, Xinfeng Li |
| 2026 | ACL | Training LLMs for Divide-and-Conquer Reasoning Elevates Test-Time Scalability. | Xiao Liang, Zhong-Zhi Li, Zhenghao Lin, Eric Hanchen Jiang, Hengyuan Zhang, Yelong Shen, Kai-Wei Chang, Ying Nian Wu, Yeyun Gong, Weizhu Chen |
| 2026 | ACL | Simple Role Assignment is Extraordinarily Effective for Safety Alignment. | Zhou Ziheng, Jiakun Ding, Zhaowei Zhang, Ruosen Gao, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong, Junqi Wang |
| 2026 | ACL | Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution. | Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong |
| 2025 | AAAI | Monitoring Primitive Interactions During the Training of DNNs. | Jie Ren, Xinhao Zheng, Jiyu Liu, Andrew Lizarraga, Ying Nian Wu, Liang Lin, Quanshi Zhang |
| 2025 | ACL | Value-Spectrum: Quantifying Preferences of Vision-Language Models via Value Decomposition in Social Media Contexts. | Jingxuan Li, Yuning Yang, Shengqi Yang, Linfan Zhang, Ying Nian Wu |
| 2025 | ICLR | Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models. | Chuan Liu, Chunshu Wu, Shihui Cao, Mingkai Chen, James Chenhao Liang, Ang Li, Michael Huang, Chuang Ren, Ying Nian Wu, Dongfang Liu, Tong Geng |
| 2025 | ICLR | SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation. | Yining Hong, Beide Liu, Maxine Wu, Yuanhao Zhai, Kai-Wei Chang, Linjie Li, Kevin Lin, Chung-Ching Lin, Jianfeng Wang, Zhengyuan Yang, Ying Nian Wu, Lijuan Wang |
| 2025 | ICLR | DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models. | Ruibing Song, Chuan Liu, Chunshu Wu, Ang Li, Dongfang Liu, Ying Nian Wu, Tong Geng |
| 2025 | ICLR | Visual Agents as Fast and Slow Thinkers. | Guangyan Sun, Mingyu Jin, Zhenting Wang, Cheng-Long Wang, Siqi Ma, Qifan Wang, Tong Geng, Ying Nian Wu, Yongfeng Zhang, Dongfang Liu |
| 2025 | ICLR | On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding. | Dehong Xu, Ruiqi Gao, Wenhao Zhang, Xue-Xin Wei, Ying Nian Wu |
| 2025 | ICML | An Expressive and Self-Adaptive Dynamical System for Efficient Function Learning. | Chuan Liu, Chunshu Wu, Ruibing Song, Ang Li, Ying Nian Wu, Tong Geng |
| 2025 | ICML | Latent Thought Models with Variational Bayes Inference-Time Computation. | Deqian Kong, Minglu Zhao, Dehong Xu, Bo Pang, Shu Wang, Edouardo Honig, Zhangzhang Si, Chuan Li, Jianwen Xie, Sirui Xie, Ying Nian Wu |
| 2025 | MICRO | DS-TIDE: Harnessing Dynamical Systems for Efficient Time-Independent Differential Equation Solving. | Chuan Liu, Chunshu Wu, Ruibing Song, Guangyan Sun, Ying Nian Wu, Yousu Chen, Ang Li, Tong Geng |
| 2025 | NAACL | Explore the Reasoning Capability of LLMs in the Chess Testbed. | Shu Wang, Lei Ji, Renxi Wang, Wenxiao Zhao, Haokun Liu, Yifan Hou, Ying Nian Wu |
| 2025 | NAACL | On the Analysis and Distillation of Emergent Outlier Properties in Pre-trained Language Models. | Tianyang Zhao, Kunwar Yashraj Singh, Srikar Appalaraju, Peng Tang, Ying Nian Wu, Li Erran Li |
| 2024 | AAAI | No Head Left Behind - Multi-Head Alignment Distillation for Transformers. | Tianyang Zhao, Kunwar Yashraj Singh, Srikar Appalaraju, Peng Tang, Vijay Mahadevan, R. Manmatha, Ying Nian Wu |
| 2024 | CVPR | Learning for Transductive Threshold Calibration in Open-World Recognition. | Qin Zhang, Dongsheng An, Tianjun Xiao, Tong He, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing |
| 2024 | ECCV | Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation. | Yingshan Chang, Yasi Zhang, Zhiyuan Fang, Ying Nian Wu, Yonatan Bisk, Feng Gao |
| 2024 | ECCV | Object-Conditioned Energy-Based Attention Map Alignment in Text-to-Image Diffusion Models. | Yasi Zhang, Peiyu Yu, Ying Nian Wu |
| 2024 | ICASSP | Long-Term Social Interaction Context: The Key to Egocentric Addressee Detection. | Deqian Kong, Furqan Khan, Xu Zhang, Prateek Singhal, Ying Nian Wu |
| 2024 | ICLR | Neural-Symbolic Recursive Machine for Systematic Generalization. | Qing Li, Yixin Zhu, Yitao Liang, Ying Nian Wu, Song-Chun Zhu, Siyuan Huang |
| 2024 | ICLR | Image Translation as Diffusion Visual Programmers. | Cheng Han, James Chenhao Liang, Qifan Wang, Majid Rabbani, Sohail A. Dianat, Raghuveer Rao, Ying Nian Wu, Dongfang Liu |
| 2024 | ICLR | Threshold-Consistent Margin Loss for Open-World Deep Metric Learning. | Qin Zhang, Linghan Xu, Jun Fang, Qingming Tang, Ying Nian Wu, Joseph Tighe, Yifan Xing |
| 2024 | ICLR | Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood. | Yaxuan Zhu, Jianwen Xie, Ying Nian Wu, Ruiqi Gao |
| 2024 | IROS | Learning Concept-Based Causal Transition and Symbolic Reasoning for Visual Planning. | Yilue Qian, Peiyu Yu, Ying Nian Wu, Yao Su, Wei Wang, Lifeng Fan |
| 2024 | IROS | LLM | Shu Wang, Muzhi Han, Ziyuan Jiao, Zeyu Zhang, Ying Nian Wu, Song-Chun Zhu, Hangxin Liu |
| 2023 | CVPR | Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. | Jiali Cui, Ying Nian Wu, Tian Han |
| 2023 | ICCV | Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. | Jiali Cui, Ying Nian Wu, Tian Han |
| 2023 | ICLR | A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics. | Qing Li, Siyuan Huang, Yining Hong, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu |
| 2023 | ICLR | Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning. | Pan Lu, Liang Qiu, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, Tanmay Rajpurohit, Peter Clark, Ashwin Kalyan |
| 2023 | ICML | On the Complexity of Bayesian Generalization. | Yu-Zhe Shi, Manjie Xu, John E. Hopcroft, Kun He, Joshua B. Tenenbaum, Song-Chun Zhu, Ying Nian Wu, Wenjuan Han, Yixin Zhu |
| 2023 | ICML | Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference. | Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang, Pascale Fung, Ying Nian Wu |
| 2023 | UAI | Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. | Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu |
| 2022 | AAAI | Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion. | Ruiqi Gao, Jianwen Xie, Siyuan Huang, Yufan Ren, Song-Chun Zhu, Ying Nian Wu |
| 2022 | AAAI | SAS: Self-Augmentation Strategy for Language Model Pre-training. | Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, Ying Nian Wu |
| 2022 | AISTATS | Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection. | Cristian I. Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot |
| 2022 | CVPR | Transform-Retrieve-Generate: Natural Language-Centric Outside-Knowledge Visual Question Answering. | Feng Gao, Qing Ping, Govind Thattai, Aishwarya N. Reganti, Ying Nian Wu, Prem Natarajan |
| 2022 | ECCV | Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning. | Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu |
| 2022 | ICLR | MCMC Should Mix: Learning Energy-Based Model with Neural Transport Latent Space MCMC. | Erik Nijkamp, Ruiqi Gao, Pavel Sountsov, Srinivas Vasudevan, Bo Pang, Song-Chun Zhu, Ying Nian Wu |
| 2022 | ICML | Latent Diffusion Energy-Based Model for Interpretable Text Modelling. | Peiyu Yu, Sirui Xie, Xiaojian Ma, Baoxiong Jia, Bo Pang, Ruiqi Gao, Yixin Zhu, Song-Chun Zhu, Ying Nian Wu |
| 2021 | AAAI | Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation. | Jianwen Xie, Zilong Zheng, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu |
| 2021 | ACL | Robust Transfer Learning with Pretrained Language Models through Adapters. | Wenjuan Han, Bo Pang, Ying Nian Wu |
| 2021 | ACL | SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues. | Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu |
| 2021 | CVPR | Trajectory Prediction With Latent Belief Energy-Based Model. | Bo Pang, Tianyang Zhao, Xu Xie, Ying Nian Wu |
| 2021 | CVPR | Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification. | Jianwen Xie, Yifei Xu, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu |
| 2021 | CVPR | Learning Neural Representation of Camera Pose with Matrix Representation of Pose Shift via View Synthesis. | Yaxuan Zhu, Ruiqi Gao, Siyuan Huang, Song-Chun Zhu, Ying Nian Wu |
| 2021 | EACL | Generative Text Modeling through Short Run Inference. | Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu |
| 2021 | ICLR | Learning Energy-Based Models by Diffusion Recovery Likelihood. | Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, Diederik P. Kingma |
| 2021 | ICML | Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification. | Bo Pang, Ying Nian Wu |
| 2021 | IJCNN | A Study of Local Optima for Learning Feature Interactions using Neural Networks. | Yangzi Guo, Ying Nian Wu, Adrian Barbu |
| 2021 | ICRA | Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance. | Xu Xie, Chi Zhang, Yixin Zhu, Ying Nian Wu, Song-Chun Zhu |
| 2021 | ICRA | Planning on a (Risk) Budget: Safe Non-Conservative Planning in Probabilistic Dynamic Environments. | Hung-Jui Huang, Kai-Chi Huang, Michal Cp, Yibiao Zhao, Ying Nian Wu, Chris L. Baker |
| 2021 | NAACL | SCRIPT: Self-Critic PreTraining of Transformers. | Erik Nijkamp, Bo Pang, Ying Nian Wu, Caiming Xiong |
| 2020 | AAAI | On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. | Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu |
| 2020 | AAAI | Motion-Based Generator Model: Unsupervised Disentanglement of Appearance, Trackable and Intrackable Motions in Dynamic Patterns. | Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu |
| 2020 | CVPR | Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. | Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu |
| 2020 | CVPR | Flow Contrastive Estimation of Energy-Based Models. | Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu |
| 2020 | CVPR | Inducing Hierarchical Compositional Model by Sparsifying Generator Network. | Xianglei Xing, Tianfu Wu, Song-Chun Zhu, Ying Nian Wu |
| 2020 | ECCV | Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. | Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu |
| 2020 | ICML | Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic Reasoning. | Qing Li, Siyuan Huang, Yining Hong, Yixin Chen, Ying Nian Wu, Song-Chun Zhu |
| 2019 | AAAI | Learning Dynamic Generator Model by Alternating Back-Propagation through Time. | Jianwen Xie, Ruiqi Gao, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu |
| 2019 | CVPR | Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. | Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu |
| 2019 | CVPR | Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. | Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu |
| 2019 | CVPR | Interpreting CNNs via Decision Trees. | Quanshi Zhang, Yu Yang, Haotian Ma, Ying Nian Wu |
| 2019 | CVPR | Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. | Tianyang Zhao, Yifei Xu, Mathew Monfort, Wongun Choi, Chris L. Baker, Yibiao Zhao, Yizhou Wang, Ying Nian Wu |
| 2019 | ICLR | Learning Grid Cells as Vector Representation of Self-Position Coupled with Matrix Representation of Self-Motion. | Ruiqi Gao, Jianwen Xie, Song-Chun Zhu, Ying Nian Wu |
| 2019 | WACV | Learning Generator Networks for Dynamic Patterns. | Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu |
| 2018 | AAAI | Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching. | Jianwen Xie, Yang Lu, Ruiqi Gao, Ying Nian Wu |
| 2018 | AAAI | Interpreting CNN Knowledge via an Explanatory Graph. | Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, Song-Chun Zhu |
| 2018 | CVPR | Learning Generative ConvNets via Multi-Grid Modeling and Sampling. | Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu |
| 2018 | CVPR | Learning Descriptor Networks for 3D Shape Synthesis and Analysis. | Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu |
| 2018 | CVPR | Interpretable Convolutional Neural Networks. | Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu |
| 2018 | ICPR | Learning Multi-view Generator Network for Shared Representation. | Tian Han, Xianglei Xing, Ying Nian Wu |
| 2018 | IJCAI | Replicating Active Appearance Model by Generator Network. | Tian Han, Jiawen Wu, Ying Nian Wu |
| 2017 | AAAI | Alternating Back-Propagation for Generator Network. | Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu |
| 2017 | AAAI | Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning. | Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu |
| 2017 | CVPR | Generative Hierarchical Learning of Sparse FRAME Models. | Jianwen Xie, Yifei Xu, Erik Nijkamp, Ying Nian Wu, Song-Chun Zhu |
| 2017 | CVPR | Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet. | Jianwen Xie, Song-Chun Zhu, Ying Nian Wu |
| 2017 | CVPR | Mining Object Parts from CNNs via Active Question-Answering. | Quanshi Zhang, Ruiming Cao, Ying Nian Wu, Song-Chun Zhu |
| 2016 | AAAI | Learning FRAME Models Using CNN Filters. | Yang Lu, Song-Chun Zhu, Ying Nian Wu |
| 2016 | ICML | A Theory of Generative ConvNet. | Jianwen Xie, Yang Lu, Song-Chun Zhu, Ying Nian Wu |
| 2015 | ICCV | Mining And-Or Graphs for Graph Matching and Object Discovery. | Quanshi Zhang, Ying Nian Wu, Song-Chun Zhu |
| 2014 | CVPR | Unsupervised Learning of Dictionaries of Hierarchical Compositional Models. | Jifeng Dai, Yi Hong, Wenze Hu, Song-Chun Zhu, Ying Nian Wu |
| 2014 | CVPR | Learning Inhomogeneous FRAME Models for Object Patterns. | Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu |
| 2013 | ICCV | Cosegmentation and Cosketch by Unsupervised Learning. | Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu |
| 2011 | ICCV | Image representation by active curves. | Wenze Hu, Ying Nian Wu, Song-Chun Zhu |
| 2009 | CVPR | Learning mixed templates for object recognition. | Zhangzhang Si, Haifeng Gong, Ying Nian Wu, Song Chun Zhu |
| 2007 | ICCV | Deformable Template As Active Basis. | Ying Nian Wu, Zhangzhang Si, Chuck Fleming, Song Chun Zhu |
| 2004 | CVPR | Information Scaling Laws in Natural Scenes. | Cheng-en Guo, Ying Nian Wu, Song Chun Zhu |
| 2003 | ICCV | Towards a Mathematical Theory of Primal Sketch and Sketchability. | Cheng-en Guo, Song Chun Zhu, Ying Nian Wu |
| 2002 | ECCV | Statistical Modeling of Texture Sketch. | Ying Nian Wu, Song Chun Zhu, Cheng-en Guo |
| 2002 | ECCV | What Are Textons? | Song Chun Zhu, Cheng-en Guo, Ying Nian Wu, Yizhou Wang |
| 2001 | CVPR | Dynamic Texture Recognition. | Payam Saisan, Gianfranco Doretto, Ying Nian Wu, Stefano Soatto |
| 2001 | ICCV | Visual Learning by Integrating Descriptive and Generative Methods. | Cheng-en Guo, Song Chun Zhu, Ying Nian Wu |
| 2001 | ICCV | Dynamic Textures. | Stefano Soatto, Gianfranco Doretto, Ying Nian Wu |
| 2000 | CVPR | Order Parameters for Minimax Entropy Distributions: When Does High Level Knowledge Help? | Alan L. Yuille, James M. Coughlan, Song Chun Zhu, Ying Nian Wu |
| 1999 | ICCV | Equivalence of Julesz and Gibbs Texture Ensembles. | Ying Nian Wu, Song Chun Zhu, Xiuwen Liu |
| 1996 | CVPR | FRAME: Filters, Random fields, and Minimax Entropy - Towards a Unified Theory for Texture Modeling. | Song Chun Zhu, Ying Nian Wu, David Mumford |