You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. - Dinghuai Zhang; Tianyuan Zhang; Yiping Lu 001; Zhanxing Zhu; Bin Dong 001 (NeurIPS, 2019)

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. - Baifeng Shi; Dinghuai Zhang; Qi Dai 001; Zhanxing Zhu; Yadong Mu; Jingdong Wang 001 (ICML, 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)

Neural Approximate Sufficient Statistics for Implicit Models. - Yanzhi Chen; Dinghuai Zhang; Michael U. Gutmann; Aaron C. Courville; Zhanxing Zhu (ICLR, 2021)

Out-of-Distribution Generalization via Risk Extrapolation (REx). - David Krueger 001; Ethan Caballero; Jörn-Henrik Jacobsen; Amy Zhang 001; Jonathan Binas; Dinghuai Zhang; Rémi Le Priol; Aaron C. Courville (ICML, 2021)

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? - Dinghuai Zhang; Kartik Ahuja; Yilun Xu; Yisen Wang 001; Aaron C. Courville (ICML, 2021)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Jean-Christophe Gagnon-Audet; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (NeurIPS, 2021)

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. - Kartik Ahuja; Ethan Caballero; Dinghuai Zhang; Yoshua Bengio; Ioannis Mitliagkas; Irina Rish (CoRR, 2021)

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond. - Dinghuai Zhang; Jie Fu 001; Yoshua Bengio; Aaron C. Courville (CoRR, 2021)

Unifying Likelihood-free Inference with Black-box Optimization and Beyond. - Dinghuai Zhang; Jie Fu 001; Yoshua Bengio; Aaron C. Courville (ICLR, 2022)

Biological Sequence Design with GFlowNets. - Moksh Jain; Emmanuel Bengio; Alex Hernández-García; Jarrid Rector-Brooks; Bonaventure F. P. Dossou; Chanakya Ajit Ekbote; Jie Fu 001; Tianyu Zhang; Michael Kilgour; Dinghuai Zhang; Lena Simine; Payel Das; et al. (ICML, 2022)

Generative Flow Networks for Discrete Probabilistic Modeling. - Dinghuai Zhang; Nikolay Malkin; Zhen Liu 019; Alexandra Volokhova; Aaron C. Courville; Yoshua Bengio (ICML, 2022)

Building Robust Ensembles via Margin Boosting. - Dinghuai Zhang; Hongyang Zhang 001; Aaron C. Courville; Yoshua Bengio; Pradeep Ravikumar; Arun Sai Suggala (ICML, 2022)

Unifying Generative Models with GFlowNets. - Dinghuai Zhang; Ricky T. Q. Chen; Nikolay Malkin; Yoshua Bengio (CoRR, 2022)

Is Nash Equilibrium Approximator Learnable? - Zhijian Duan 001; Wenhan Huang; Dinghuai Zhang; Yali Du 001; Jun Wang 012; Yaodong Yang 001; Xiaotie Deng (AAMAS, 2023)

Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. - Yimu Wang; Dinghuai Zhang; Yihan Wu; Heng Huang; Hongyang Zhang 001 (CVPR, 2023)

GFlowNets and variational inference. - Nikolay Malkin; Salem Lahlou; Tristan Deleu; Xu Ji; Edward J. Hu; Katie Everett; Dinghuai Zhang; Yoshua Bengio (ICLR, 2023)

Generative Augmented Flow Networks. - Ling Pan; Dinghuai Zhang; Aaron C. Courville; Longbo Huang; Yoshua Bengio (ICLR, 2023)

Predictive Inference with Feature Conformal Prediction. - Jiaye Teng; Chuan Wen; Dinghuai Zhang; Yoshua Bengio; Yang Gao 029; Yang Yuan (ICLR, 2023)

Latent State Marginalization as a Low-cost Approach for Improving Exploration. - Dinghuai Zhang; Aaron C. Courville; Yoshua Bengio; Qinqing Zheng; Amy Zhang 001; Ricky T. Q. Chen (ICLR, 2023)

A theory of continuous generative flow networks. - Salem Lahlou; Tristan Deleu; Pablo Lemos; Dinghuai Zhang; Alexandra Volokhova; Alex Hernández-García; Léna Néhale Ezzine; Yoshua Bengio; Nikolay Malkin (ICML, 2023)

GFlowOut: Dropout with Generative Flow Networks. - Dianbo Liu; Moksh Jain; Bonaventure F. P. Dossou; Qianli Shen; Salem Lahlou; Anirudh Goyal; Nikolay Malkin; Chris Chinenye Emezue; Dinghuai Zhang; Nadhir Hassen; Xu Ji; Kenji Kawaguchi; et al. (ICML, 2023)

Better Training of GFlowNets with Local Credit and Incomplete Trajectories. - Ling Pan; Nikolay Malkin; Dinghuai Zhang; Yoshua Bengio (ICML, 2023)

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (NeurIPS, 2023)

Stochastic Generative Flow Networks. - Ling Pan; Dinghuai Zhang; Moksh Jain; Longbo Huang; Yoshua Bengio (UAI, 2023)

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. - Dinghuai Zhang; Hanjun Dai; Nikolay Malkin; Aaron C. Courville; Yoshua Bengio; Ling Pan (CoRR, 2023)

Learning to Scale Logits for Temperature-Conditional GFlowNets. - Minsu Kim 004; Joohwan Ko; Dinghuai Zhang; Ling Pan; Taeyoung Yun; Woochang Kim; Jinkyoo Park; Yoshua Bengio (CoRR, 2023)

Rare Event Probability Learning by Normalizing Flows. - Zhengqi Gao; Dinghuai Zhang; Luca Daniel; Duane S. Boning (CoRR, 2023)

NOFIS: Normalizing Flow for Rare Circuit Failure Analysis. - Zhengqi Gao; Dinghuai Zhang; Luca Daniel; Duane S. Boning (DAC, 2024)

Delta-AI: Local objectives for amortized inference in sparse graphical models. - Jean-Pierre R. Falet; Hae Beom Lee; Nikolay Malkin; Chen Sun 007; Dragos Secrieru; Dinghuai Zhang; Guillaume Lajoie; Yoshua Bengio (ICLR, 2024)

Local Search GFlowNets. - Minsu Kim 004; Taeyoung Yun; Emmanuel Bengio; Dinghuai Zhang; Yoshua Bengio; Sungsoo Ahn; Jinkyoo Park (ICLR, 2024)

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. - Dinghuai Zhang; Ricky T. Q. Chen; Cheng-Hao Liu; Aaron C. Courville; Yoshua Bengio (ICLR, 2024)

PhyloGFN: Phylogenetic inference with generative flow networks. - Ming-Yang Zhou; Zichao Yan; Elliot Layne; Nikolay Malkin; Dinghuai Zhang; Moksh Jain; Mathieu Blanchette; Yoshua Bengio (ICLR, 2024)

Learning to Scale Logits for Temperature-Conditional GFlowNets. - Minsu Kim 004; Joohwan Ko; Taeyoung Yun; Dinghuai Zhang; Ling Pan; Woochang Kim; Jinkyoo Park; Emmanuel Bengio; Yoshua Bengio (ICML, 2024)

Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. - Peiyu Yu; Dinghuai Zhang; Hengzhi He; Xiaojian Ma 001; Ruiyao Miao; Yifan Lu; Yasi Zhang; Deqian Kong; Ruiqi Gao; Jianwen Xie; Guang Cheng 003; Ying Nian Wu (CoRR, 2024)

Baking Symmetry into GFlowNets. - George Ma; Emmanuel Bengio; Yoshua Bengio; Dinghuai Zhang (CoRR, 2024)

EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics. - Chenqing Hua; Yong Liu; Dinghuai Zhang; Odin Zhang; Sitao Luan; Kevin K. Yang; Guy Wolf; Doina Precup; Shuangjia Zheng (CoRR, 2024)

DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation. - Jiatao Gu; Yuyang Wang; Yizhe Zhang 002; Qihang Zhang; Dinghuai Zhang; Navdeep Jaitly; Josh M. Susskind; Shuangfei Zhai (CoRR, 2024)

DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting. - Eric Hanchen Jiang; Zhi Zhang 012; Dinghuai Zhang; Andrew Lizarraga; Chenheng Xu; Yasi Zhang; Siyan Zhao; Zhengjie Xu; Peiyu Yu; Yuer Tang; Deqian Kong; Ying Nian Wu (CoRR, 2024)

Distributional GFlowNets with Quantile Flows. - Dinghuai Zhang; Ling Pan; Ricky T. Q. Chen; Aaron C. Courville; Yoshua Bengio (Trans. Mach. Learn. Res., 2024)

Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation. - Taeyoung Yun; Dinghuai Zhang; Jinkyoo Park; Ling Pan (CVPR, 2025)

Denoising Autoregressive Transformers for Scalable Text-to-Image Generation. - Jiatao Gu; Yuyang Wang; Yizhe Zhang 002; Qihang Zhang; Dinghuai Zhang; Navdeep Jaitly; Joshua M. Susskind; Shuangfei Zhai (ICLR, 2025)

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets. - Zhen Liu 019; Tim Z. Xiao; Weiyang Liu; Yoshua Bengio; Dinghuai Zhang (ICLR, 2025)

Improving GFlowNets for Text-to-Image Diffusion Alignment. - Dinghuai Zhang; Yizhe Zhang 002; Jiatao Gu; Ruixiang Zhang; Joshua M. Susskind; Navdeep Jaitly; Shuangfei Zhai (Trans. Mach. Learn. Res., 2025)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Dinghuai Zhang; Shreyas Padhy; Ruikang Ouyang; Carla P. Gomes; José Miguel Hernández-Lobato (CoRR, 2025)

Self-Evolving Curriculum for LLM Reasoning. - Xiaoyin Chen; Jiarui Lu; Minsu Kim 004; Dinghuai Zhang; Jian Tang 005; Alexandre Piché; Nicolas Gontier; Yoshua Bengio; Ehsan Kamalloo (CoRR, 2025)

Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards. - Qingming Liu; Zhen Liu; Dinghuai Zhang; Kui Jia (CoRR, 2025)