Dinghuai Zhang
29 papers · 2019–2025 · 5 venues · across top CS/AI venues
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(20)
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(5)
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Venues
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Research topics
Machine Learning
(26)
Deep Learning
(18)
Artificial Intelligence
(17)
Optimization & Theory
(16)
Models
(13)
Core AI
(11)
Learning Types Β· Machine Learning
(11)
Bayesian & Probabilistic Β· Artificial Intelligence
(8)
Core Methods
(8)
Learning Types Β· Deep Learning
(6)
Bayesian & Probabilistic Β· Machine Learning
(5)
Mathematics & Optimization
(4)
Reinforcement Learning
(4)
Application Areas
(4)
Methods
(4)
Keywords
generative flow network
(14)
variational inference
(6)
amortized inference
(4)
probabilistic modeling
(4)
generative model
(3)
reward function
(3)
out-of-distribution generalization
(3)
domain generalization
(3)
credit assignment
(3)
adversarial robustness
(3)
energy-based model
(2)
adversarial attack
(2)
adversarial perturbation
(2)
diffusion model
(2)
stochastic process
(2)
likelihood-free inference
(2)
adversarial training
(2)
reinforcement learning
(2)
bayesian inference
(2)
text-to-image generation
(2)
Papers
Denoising Autoregressive Transformers for Scalable Text-to-Image Generation
ICLR 2025
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets
ICLR 2025
Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
CVPR 2025
Delta-AI: Local objectives for amortized inference in sparse graphical models
ICLR 2024
Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
ICLR 2024
Learning to Scale Logits for Temperature-Conditional GFlowNets
ICML 2024
Local Search GFlowNets
ICLR 2024
PhyloGFN: Phylogenetic inference with generative flow networks
ICLR 2024
A theory of continuous generative flow networks
ICML 2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
ICML 2023
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets
CVPR 2023
Generative Augmented Flow Networks
ICLR 2023
GFlowNets and variational inference
ICLR 2023
GFlowOut: Dropout with Generative Flow Networks
ICML 2023
Latent State Marginalization as a Low-cost Approach for Improving Exploration
ICLR 2023
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
NeurIPS 2023
Predictive Inference with Feature Conformal Prediction
ICLR 2023
Stochastic Generative Flow Networks
UAI 2023
Biological Sequence Design with GFlowNets
ICML 2022
Building Robust Ensembles via Margin Boosting
ICML 2022
Generative Flow Networks for Discrete Probabilistic Modeling
ICML 2022
Unifying Likelihood-free Inference with Black-box Optimization and Beyond
ICLR 2022
Can Subnetwork Structure Be the Key to Out-of-Distribution Generalization?
ICML 2021
Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization
NeurIPS 2021
Neural Approximate Sufficient Statistics for Implicit Models
ICLR 2021
Out-of-Distribution Generalization via Risk Extrapolation (REx)
ICML 2021
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework
NeurIPS 2020
Informative Dropout for Robust Representation Learning: A Shape-bias Perspective
ICML 2020
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
NeurIPS 2019