Dinghuai Zhang 张鼎怀
Dinghuai Zhang 张鼎怀
Other namesDinghuai Zhang
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Cited by
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Out-of-distribution generalization via risk extrapolation (rex)
D Krueger, E Caballero, JH Jacobsen, A Zhang, J Binas, D Zhang, ...
ICML 2021 Long talk, 2020
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
D Zhang, T Zhang, Y Lu, Z Zhu, B Dong
NeurIPS 2019; arXiv preprint arXiv:1905.00877, 2019
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
K Ahuja, E Caballero, D Zhang, JC Gagnon-Audet, Y Bengio, I Mitliagkas, ...
NeurIPS 2021 spotlight; arXiv preprint arXiv:2106.06607, 2021
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
B Shi, D Zhang, Q Dai, Z Zhu, Y Mu, J Wang
ICML 2020, 2020
Can Subnetwork Structure be the Key to Out-of-Distribution Generalization?
D Zhang, K Ahuja, Y Xu, Y Wang, A Courville
ICML 2021 long talk; arXiv preprint arXiv:2106.02890, 2021
Black-box certification with randomized smoothing: A functional optimization based framework
D Zhang, M Ye, C Gong, Z Zhu, Q Liu
NeurIPS 2020, 2020
Neural Approximate Sufficient Statistics for Implicit Models
Y Chen*, D Zhang*, M Gutmann, A Courville, Z Zhu
ICLR 2021 spotlight; arXiv preprint arXiv:2010.10079, 2020
Biological Sequence Design with GFlowNets
M Jain, E Bengio, AH Garcia, J Rector-Brooks, BFP Dossou, C Ekbote, ...
ICML 2022; arXiv preprint arXiv:2203.04115, 2022
Generative Flow Networks for Discrete Probabilistic Modeling
D Zhang, N Malkin, Z Liu, A Volokhova, A Courville, Y Bengio
ICML 2022; arXiv preprint arXiv:2202.01361, 2022
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
D Zhang, J Fu, Y Bengio, A Courville
ICLR 2022 spotlight; arXiv preprint arXiv:2110.03372, 2021
Building Robust Ensembles via Margin Boosting
D Zhang, H Zhang, A Courville, Y Bengio, P Ravikumar, AS Suggala
ICML 2022; arXiv preprint arXiv:2206.03362, 2022
GFlowNets and variational inference
N Malkin, S Lahlou, T Deleu, X Ji, E Hu, K Everett, D Zhang, Y Bengio
ICLR 2023; arXiv preprint arXiv:2210.00580, 2022
Predictive Inference with Feature Conformal Prediction
J Teng, C Wen, D Zhang, Y Bengio, Y Gao, Y Yuan
ICLR 2023; arXiv preprint arXiv:2210.00173, 2022
A theory of continuous generative flow networks
S Lahlou, T Deleu, P Lemos, D Zhang, A Volokhova, A Hernández-García, ...
ICML 2023; arXiv preprint arXiv:2301.12594, 2023
Unifying Generative Models with GFlowNets and Beyond
D Zhang, RTQ Chen, N Malkin, Y Bengio
arXiv preprint arXiv:2209.02606, 2022
Better training of gflownets with local credit and incomplete trajectories
L Pan, N Malkin, D Zhang, Y Bengio
ICML 2023; arXiv preprint arXiv:2302.01687, 2023
GFlowOut: Dropout with Generative Flow Networks
D Liu, M Jain, B Dossou, Q Shen, S Lahlou, A Goyal, N Malkin, C Emezue, ...
ICML 2023; arXiv preprint arXiv:2210.12928, 2022
Latent State Marginalization as a Low-cost Approach for Improving Exploration
D Zhang, A Courville, Y Bengio, Q Zheng, A Zhang, RTQ Chen
ICLR 2023; arXiv preprint arXiv:2210.00999, 2022
Is Nash Equilibrium Approximator Learnable?
Z Duan, W Huang, D Zhang, Y Du, Y Yang, J Wang, X Deng
AAMAS 2023; arXiv preprint arXiv:2108.07472, 2021
Generative Augmented Flow Networks
L Pan, D Zhang, A Courville, L Huang, Y Bengio
ICLR 2023 spotlight; arXiv preprint arXiv:2210.03308, 2022
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