Zhang, Dinghuai

41 publications

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

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

TMLR 2025 Improving GFlowNets for Text-to-Image Diffusion Alignment Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai

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

NeurIPS 2025 Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards Qingming Liu, Zhen Liu, Dinghuai Zhang, Kui Jia

NeurIPS 2025 Next Semantic Scale Prediction via Hierarchical Diffusion Language Models Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, Tommi Jaakkola

ICLRW 2025 No Trick, No Treat: Pursuits and Challenges Towards Simulation-Free Training of Neural Samplers Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla P Gomes, José Miguel Hernández-Lobato

NeurIPS 2025 Value Gradient Guidance for Flow Matching Alignment Zhen Liu, Tim Z. Xiao, Carles Domingo-Enrich, Weiyang Liu, Dinghuai Zhang

ICLR 2024 Delta-AI: Local Objectives for Amortized Inference in Sparse Graphical Models Jean-Pierre René Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio

ICLR 2024 Diffusion Generative Flow Samplers: Improving Learning Signals Through Partial Trajectory Optimization Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio

TMLR 2024 Distributional GFlowNets with Quantile Flows Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron Courville, Yoshua Bengio

NeurIPSW 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

ICMLW 2024 Improving GFlowNets for Text-to-Image Diffusion Alignment Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai

ICMLW 2024 Improving GFlowNets for Text-to-Image Diffusion Alignment Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai

ICML 2024 Learning to Scale Logits for Temperature-Conditional GFlowNets Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woo Chang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio

ICLR 2024 Local Search GFlowNets Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park

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

ICML 2023 A Theory of Continuous Generative Flow Networks Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-Garcı́a, Lena Nehale Ezzine, Yoshua Bengio, Nikolay Malkin

NeurIPSW 2023 Baking Symmetry into GFlowNets George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang

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

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

ICLR 2023 GFlowNets and Variational Inference Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J Hu, Katie E Everett, Dinghuai Zhang, Yoshua Bengio

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, Yoshua Bengio

ICLR 2023 Generative Augmented Flow Networks Ling Pan, Dinghuai Zhang, Aaron Courville, Longbo Huang, Yoshua Bengio

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

NeurIPSW 2023 Learning to Scale Logits for Temperature-Conditional GFlowNets Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woo Chang Kim, Jinkyoo Park, Yoshua Bengio

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

ICLR 2023 Predictive Inference with Feature Conformal Prediction Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan

UAI 2023 Stochastic Generative Flow Networks Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

ICML 2022 Biological Sequence Design with GFlowNets Moksh Jain, Emmanuel Bengio, Alex Hernandez-Garcia, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

ICML 2022 Building Robust Ensembles via Margin Boosting Dinghuai Zhang, Hongyang Zhang, Aaron Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

NeurIPSW 2022 Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness by Adaptive Budgets Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang

ICML 2022 Generative Flow Networks for Discrete Probabilistic Modeling Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio

ICLR 2022 Unifying Likelihood-Free Inference with Black-Box Optimization and Beyond Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron Courville

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

NeurIPS 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

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

ICML 2021 Out-of-Distribution Generalization via Risk Extrapolation (REx) David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, Aaron Courville

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

ICML 2020 Informative Dropout for Robust Representation Learning: A Shape-Bias Perspective Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

NeurIPS 2019 You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong