Dinghuai Zhang

80 papers A* 29A 2Journal 49

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diffusion Large Language Models for Black-Box Optimization. CoRR Ye Yuan, Can Chen, Zipeng Sun, Dinghuai Zhang, Christopher Pal, Xue Liu
2026	J	`jnl`	Scaling Reward Modeling without Human Supervision. CoRR Jingxuan Fan, Yueying Li, Zhenting Qi, Dinghuai Zhang, Kianté Brantley, Sham M. Kakade, Hanlin Zhang
2026	J	`jnl`	Test-time Recursive Thinking: Self-Improvement without External Feedback. CoRR Yufan Zhuang, Chandan Singh, Liyuan Liu, Yelong Shen, Dinghuai Zhang, Jingbo Shang, Jianfeng Gao, Weizhu Chen
2025	J	`jnl`	Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner. CoRR Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang, Chubin Zhang, Muhan Zhang, Lester Mackey, Tommi S. Jaakkola, Stephen Bates, Dinghuai Zhang
2025	A*	`conf`	Denoising Autoregressive Transformers for Scalable Text-to-Image Generation. ICLR Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Joshua M. Susskind, Shuangfei Zhai
2025	A*	`conf`	Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets. ICLR Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang
2025	J	`jnl`	FlowRL: Matching Reward Distributions for LLM Reasoning. CoRR Xuekai Zhu, Daixuan Cheng, Dinghuai Zhang, Hengli Li, Kaiyan Zhang, Che Jiang, Youbang Sun, Ermo Hua, Yuxin Zuo, Xingtai Lv, Qizheng Zhang, Lin Chen, Fanghao Shao, Bo Xue, Yunchong Song, Zhenjie Yang, Ganqu Cui, Ning Ding, Jianfeng Gao, Xiaodong Liu, Bowen Zhou, Hongyuan Mei, Zhouhan Lin
2025	J	`jnl`	Generalized Dynamics Generation towards Scannable Physical World Model. CoRR Yichen Li, Zhiyi Li, Brandon Feng, Dinghuai Zhang, Antonio Torralba
2025	J	`jnl`	Improving GFlowNets for Text-to-Image Diffusion Alignment. Trans. Mach. Learn. Res. Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Joshua M. Susskind, Navdeep Jaitly, Shuangfei Zhai
2025	A*	`conf`	Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation. CVPR Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, Ling Pan
2025	J	`jnl`	Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation. CoRR Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, Ling Pan
2025	J	`jnl`	Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards. CoRR Qingming Liu, Zhen Liu, Dinghuai Zhang, Kui Jia
2025	J	`jnl`	Next Semantic Scale Prediction via Hierarchical Diffusion Language Models. CoRR Cai Zhou, Chenyu Wang, Dinghuai Zhang, Shangyuan Tong, Yifei Wang, Stephen Bates, Tommi S. Jaakkola
2025	J	`jnl`	No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. CoRR Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, Ruikang Ouyang, Carla P. Gomes, José Miguel Hernández-Lobato
2025	J	`jnl`	Self-Evolving Curriculum for LLM Reasoning. CoRR Xiaoyin Chen, Jiarui Lu, Minsu Kim, Dinghuai Zhang, Jian Tang, Alexandre Piché, Nicolas Gontier, Yoshua Bengio, Ehsan Kamalloo
2025	J	`jnl`	Value Gradient Guidance for Flow Matching Alignment. CoRR Zhen Liu, Tim Z. Xiao, Carles Domingo-Enrich, Weiyang Liu, Dinghuai Zhang
2024	J	`jnl`	Baking Symmetry into GFlowNets. CoRR George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang
2024	J	`jnl`	DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation. CoRR Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh M. Susskind, Shuangfei Zhai
2024	J	`jnl`	DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting. CoRR Eric Hanchen Jiang, Zhi Zhang, Dinghuai Zhang, Andrew Lizarraga, Chenheng Xu, Yasi Zhang, Siyan Zhao, Zhengjie Xu, Peiyu Yu, Yuer Tang, Deqian Kong, Ying Nian Wu
2024	A*	`conf`	Delta-AI: Local objectives for amortized inference in sparse graphical models. ICLR Jean-Pierre R. Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
2024	A*	`conf`	Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. ICLR Dinghuai Zhang, Ricky T. Q. Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio
2024	J	`jnl`	Distributional GFlowNets with Quantile Flows. Trans. Mach. Learn. Res. Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
2024	J	`jnl`	Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets. CoRR Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang
2024	J	`jnl`	EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics. CoRR Chenqing Hua, Yong Liu, Dinghuai Zhang, Odin Zhang, Sitao Luan, Kevin K. Yang, Guy Wolf, Doina Precup, Shuangjia Zheng
2024	J	`jnl`	Improving GFlowNets for Text-to-Image Diffusion Alignment. CoRR Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh M. Susskind, Navdeep Jaitly, Shuangfei Zhai
2024	J	`jnl`	Latent Energy-Based Odyssey: Black-Box Optimization via Expanded Exploration in the Energy-Based Latent Space. CoRR Peiyu Yu, Dinghuai Zhang, Hengzhi He, Xiaojian Ma, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu
2024	A*	`conf`	Learning to Scale Logits for Temperature-Conditional GFlowNets. ICML Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio
2024	A*	`conf`	Local Search GFlowNets. ICLR Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
2024	A*	`conf`	NOFIS: Normalizing Flow for Rare Circuit Failure Analysis. DAC Zhengqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning
2024	A*	`conf`	PhyloGFN: Phylogenetic inference with generative flow networks. ICLR Ming-Yang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio
2023	A*	`conf`	A theory of continuous generative flow networks. ICML Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
2023	J	`jnl`	A theory of continuous generative flow networks. CoRR Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernández-García, Léna Néhale Ezzine, Yoshua Bengio, Nikolay Malkin
2023	A*	`conf`	Better Training of GFlowNets with Local Credit and Incomplete Trajectories. ICML Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
2023	J	`jnl`	Better Training of GFlowNets with Local Credit and Incomplete Trajectories. CoRR Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio
2023	A*	`conf`	Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. CVPR Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang
2023	J	`jnl`	Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets. CoRR Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang
2023	J	`jnl`	Delta-AI: Local objectives for amortized inference in sparse graphical models. CoRR Jean-Pierre R. Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio
2023	J	`jnl`	Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization. CoRR Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron C. Courville, Yoshua Bengio
2023	J	`jnl`	Distributional GFlowNets with Quantile Flows. CoRR Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio
2023	A*	`conf`	GFlowNets and variational inference. ICLR Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
2023	A*	`conf`	GFlowOut: Dropout with Generative Flow Networks. ICML 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
2023	A*	`conf`	Generative Augmented Flow Networks. ICLR Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio
2023	A	`conf`	Is Nash Equilibrium Approximator Learnable? AAMAS Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng
2023	A*	`conf`	Latent State Marginalization as a Low-cost Approach for Improving Exploration. ICLR Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
2023	J	`jnl`	Learning to Scale Logits for Temperature-Conditional GFlowNets. CoRR Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, Yoshua Bengio
2023	J	`jnl`	Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. CoRR Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
2023	A*	`conf`	Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets. NeurIPS Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
2023	J	`jnl`	Local Search GFlowNets. CoRR Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
2023	J	`jnl`	PhyloGFN: Phylogenetic inference with generative flow networks. CoRR Mingyang Zhou, Zichao Yan, Elliot Layne, Nikolay Malkin, Dinghuai Zhang, Moksh Jain, Mathieu Blanchette, Yoshua Bengio
2023	A*	`conf`	Predictive Inference with Feature Conformal Prediction. ICLR Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan
2023	J	`jnl`	Rare Event Probability Learning by Normalizing Flows. CoRR Zhengqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning
2023	A	`conf`	Stochastic Generative Flow Networks. UAI Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio
2023	J	`jnl`	Stochastic Generative Flow Networks. CoRR Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio
2022	A*	`conf`	Biological Sequence Design with GFlowNets. ICML Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ajit Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
2022	J	`jnl`	Biological Sequence Design with GFlowNets. CoRR Moksh Jain, Emmanuel Bengio, Alex Hernández-García, Jarrid Rector-Brooks, Bonaventure F. P. Dossou, Chanakya Ekbote, Jie Fu, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio
2022	A*	`conf`	Building Robust Ensembles via Margin Boosting. ICML Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
2022	J	`jnl`	Building Robust Ensembles via Margin Boosting. CoRR Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala
2022	J	`jnl`	GFlowNets and variational inference. CoRR Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio
2022	J	`jnl`	GFlowOut: Dropout with Generative Flow Networks. CoRR Dianbo Liu, Moksh Jain, Bonaventure Dossou, Qianli Shen, Salem Lahlou, Anirudh Goyal, Nikolay Malkin, Chris Emezue, Dinghuai Zhang, Nadhir Hassen, Xu Ji, Kenji Kawaguchi, Yoshua Bengio
2022	J	`jnl`	Generative Augmented Flow Networks. CoRR Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio
2022	A*	`conf`	Generative Flow Networks for Discrete Probabilistic Modeling. ICML Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio
2022	J	`jnl`	Generative Flow Networks for Discrete Probabilistic Modeling. CoRR Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio
2022	J	`jnl`	Latent State Marginalization as a Low-cost Approach for Improving Exploration. CoRR Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
2022	J	`jnl`	Predictive Inference with Feature Conformal Prediction. CoRR Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan
2022	J	`jnl`	Unifying Generative Models with GFlowNets. CoRR Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio
2022	A*	`conf`	Unifying Likelihood-free Inference with Black-box Optimization and Beyond. ICLR Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville
2021	A*	`conf`	Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization? ICML Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville
2021	J	`jnl`	Can Subnetwork Structure be the Key to Out-of-Distribution Generalization? CoRR Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville
2021	A*	`conf`	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. NeurIPS Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
2021	J	`jnl`	Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization. CoRR Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Yoshua Bengio, Ioannis Mitliagkas, Irina Rish
2021	A*	`conf`	Neural Approximate Sufficient Statistics for Implicit Models. ICLR Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
2021	A*	`conf`	Out-of-Distribution Generalization via Risk Extrapolation (REx). ICML David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville
2021	J	`jnl`	Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond. CoRR Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville
2020	A*	`conf`	Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. NeurIPS Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
2020	J	`jnl`	Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework. CoRR Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
2020	A*	`conf`	Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. ICML Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
2020	J	`jnl`	Informative Dropout for Robust Representation Learning: A Shape-bias Perspective. CoRR Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang
2020	J	`jnl`	Neural Approximate Sufficient Statistics for Implicit Models. CoRR Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu
2019	A*	`conf`	You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. NeurIPS Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
2019	J	`jnl`	You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle. CoRR Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong