Dinghuai Zhang 张鼎怀
Dinghuai Zhang 张鼎怀
Other namesDinghuai Zhang
Mila, University of Montreal
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
D Zhang, H Dai, N Malkin, A Courville, Y Bengio, L Pan
NeurIPS 2023 spotlight; arXiv preprint arXiv:2305.17010, 2023
Generative Augmented Flow Networks
L Pan, D Zhang, A Courville, L Huang, Y Bengio
ICLR 2023 spotlight; arXiv preprint arXiv:2210.03308, 2022
Unifying Generative Models with GFlowNets and Beyond
D Zhang, RTQ Chen, N Malkin, Y Bengio
ICML 2022 Beyond Bayes workshop; arXiv preprint arXiv:2209.02606, 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
Stochastic Generative Flow Networks
L Pan, D Zhang, M Jain, L Huang, Y Bengio
UAI 2023 spotlight; arXiv preprint arXiv:2302.09465, 2023
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
Distributional GFlowNets with Quantile Flows
D Zhang, L Pan, RTQ Chen, A Courville, Y Bengio
TMLR; arXiv preprint arXiv:2302.05793, 2023
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
The system can't perform the operation now. Try again later.
Articles 1–20