Dinghuai Zhang

According to our database, Dinghuai Zhang authored at least 42 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Distributional GFlowNets with Quantile Flows.

Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen, Aaron C. Courville, Yoshua Bengio

Trans. Mach. Learn. Res., 2024

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets.

Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua Bengio, Dinghuai Zhang

CoRR, 2024

DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting.

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

CoRR, 2024

DART: Denoising Autoregressive Transformer for Scalable Text-to-Image Generation.

Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh M. Susskind, Shuangfei Zhai

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

Baking Symmetry into GFlowNets.

George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang

CoRR, 2024

Improving GFlowNets for Text-to-Image Diffusion Alignment.

Dinghuai Zhang, Yizhe Zhang, Jiatao Gu, Ruixiang Zhang, Josh M. Susskind, Navdeep Jaitly, Shuangfei Zhai

CoRR, 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, Ruiyao Miao, Yifan Lu, Yasi Zhang, Deqian Kong, Ruiqi Gao, Jianwen Xie, Guang Cheng, Ying Nian Wu

CoRR, 2024

Learning to Scale Logits for Temperature-Conditional GFlowNets.

Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Twelfth International Conference on Learning Representations, 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

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Local Search GFlowNets.

Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Delta-AI: Local objectives for amortized inference in sparse graphical models.

Jean-Pierre R. Falet, Hae Beom Lee, Nikolay Malkin, Chen Sun, Dragos Secrieru, Dinghuai Zhang, Guillaume Lajoie, Yoshua Bengio

Proceedings of the Twelfth International Conference on Learning Representations, 2024

NOFIS: Normalizing Flow for Rare Circuit Failure Analysis.

Zhengqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning

Proceedings of the 61st ACM/IEEE Design Automation Conference, 2024

2023

Rare Event Probability Learning by Normalizing Flows.

Zhengqi Gao, Dinghuai Zhang, Luca Daniel, Duane S. Boning

CoRR, 2023

Learning to Scale Logits for Temperature-Conditional GFlowNets.

Minsu Kim, Joohwan Ko, Dinghuai Zhang, Ling Pan, Taeyoung Yun, Woochang Kim, Jinkyoo Park, Yoshua Bengio

CoRR, 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

Stochastic Generative Flow Networks.

Ling Pan, Dinghuai Zhang, Moksh Jain, Longbo Huang, Yoshua Bengio

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets.

Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Better Training of GFlowNets with Local Credit and Incomplete Trajectories.

Ling Pan, Nikolay Malkin, Dinghuai Zhang, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Latent State Marginalization as a Low-cost Approach for Improving Exploration.

Dinghuai Zhang, Aaron C. Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Predictive Inference with Feature Conformal Prediction.

Jiaye Teng, Chuan Wen, Dinghuai Zhang, Yoshua Bengio, Yang Gao, Yang Yuan

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Generative Augmented Flow Networks.

Ling Pan, Dinghuai Zhang, Aaron C. Courville, Longbo Huang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

GFlowNets and variational inference.

Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward J. Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets.

Yimu Wang, Dinghuai Zhang, Yihan Wu, Heng Huang, Hongyang Zhang

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Is Nash Equilibrium Approximator Learnable?

Zhijian Duan, Wenhan Huang, Dinghuai Zhang, Yali Du, Jun Wang, Yaodong Yang, Xiaotie Deng

Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, 2023

2022

Unifying Generative Models with GFlowNets.

Dinghuai Zhang, Ricky T. Q. Chen, Nikolay Malkin, Yoshua Bengio

CoRR, 2022

Building Robust Ensembles via Margin Boosting.

Dinghuai Zhang, Hongyang Zhang, Aaron C. Courville, Yoshua Bengio, Pradeep Ravikumar, Arun Sai Suggala

Proceedings of the International Conference on Machine Learning, 2022

Generative Flow Networks for Discrete Probabilistic Modeling.

Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron C. Courville, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 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, Tianyu Zhang, Michael Kilgour, Dinghuai Zhang, Lena Simine, Payel Das, Yoshua Bengio

Proceedings of the International Conference on Machine Learning, 2022

Unifying Likelihood-free Inference with Black-box Optimization and Beyond.

Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond.

Dinghuai Zhang, Jie Fu, Yoshua Bengio, Aaron C. Courville

CoRR, 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

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?

Dinghuai Zhang, Kartik Ahuja, Yilun Xu, Yisen Wang, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Out-of-Distribution Generalization via Risk Extrapolation (REx).

David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, Aaron C. Courville

Proceedings of the 38th International Conference on Machine Learning, 2021

Neural Approximate Sufficient Statistics for Implicit Models.

Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron C. Courville, Zhanxing Zhu

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework.

Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Informative Dropout for Robust Representation Learning: A Shape-bias Perspective.

Baifeng Shi, Dinghuai Zhang, Qi Dai, Zhanxing Zhu, Yadong Mu, Jingdong Wang

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle.

Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019