Resources

Here is a summary of recent advancements in the field of GFlowNet that could be helpful for future developments. If you have a paper or repository that you would like to be included, please inform us (gflownet.workshop@mila.quebec).

Papers

Conferences and Journals

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. Emmanuel Bengio, et al. NeurIPS 2021.
Generative Flow Networks for Discrete Probabilistic Modeling. Dinghuai Zhang, et al. ICML 2022.
Biological Sequence Design with GFlowNets. Moksh Jain, et al. ICML 2022.
Bayesian Structure Learning with Generative Flow Networks. Tristan Deleu, et al. UAI 2022.
Trajectory Balance: Improved Credit Assignment in GFlowNets. Nikolay Malkin, et al. NeurIPS 2022.
Generative Augmented Flow Networks. Ling Pan, et al. ICLR 2023.
GFlowNets and Variational Inference. Nikolay Malkin, et al. ICLR 2023.
Robust Scheduling with GFlowNets. David W. Zhang, et al. ICLR 2023.
DAG Matters! GFlowNets Enhanced Explainer For Graph Neural Networks. Wenqian Li, et al. ICLR 2023.
GFlowNet Foundations. Yoshua Bengio, et al. JMLR 2023.
GFlowNets for AI-Driven Scientific Discovery. Moksh Jain, et al. Digital Discovery 2023.
Better Training of GFlowNets with Local Credit and Incomplete Trajectories. Ling Pan, et al. ICML 2023.
Learning GFlowNets from Partial Episodes for Improved Convergence and Stability. Kanika Madan,et al. ICML 2023.
Multi-Objective GFlowNets. Moksh Jain, et al. ICML 2023.
A Theory of Continuous Generative Flow Networks. Salem Lahlou, et al. ICML 2023.
GFlowNet-EM for Learning Compositional Latent Variable Models. Edward Hu, et al. ICML 2023.
GFlowOut: Dropout with Generative Flow Networks. Dianbo Liu, et al. ICML 2023.
Towards Understanding and Improving GFlowNet Training. Max W. Shen, et al. ICML 2023.
Stochastic Generative Flow Networks. Ling Pan, et al. UAI 2023.
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network. Tristan Deleu, et al. NeurIPS 2023.
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets. Dinghuai Zhang, et al. NeurIPS 2023.
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets. Lazar Atanackovic, et al. NeurIPS 2023.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets. Yiheng Zhu, et al. NeurIPS 2023.
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning. Chong Zhang, et al. ICRA 2023.
A Variational Perspective on Generative Flow Networks. Heiko Zimmermann, et al. TMLR 2023.
Generative Flow Network for Listwise Recommendation. Shuchang Liu, et al. KDD 2023.
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs. Yinchuan Li, et al. IJCAI 2023.
Diverse Policy Optimization for Structured Action Space. Wenhao Li, et al. AAMAS 2023.
TBD

Workshops

Evaluating Generalization in GFlowNets for Molecule Design. Andrei Cristian Nica, et al. ICML 2022 Machine Learning for Drug Discovery workshop.
Unifying Generative Models with GFlowNets and Beyond. Dinghuai Zhang, et al. ICML 2022 Beyond Bayes: Paths Towards Universal Reasoning Systems workshop.
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions. Chanakya Ekbote, et al. NeurIPS 2022 Human in the Loop Learning workshop.
Bayesian Dynamic Causal Discovery. Alexander Tong, et al. NeurIPS 2022 A Causal View on Dynamical Systems workshop.
An Empirical Study of the Effectiveness of Using a Replay Buffer on Mode Discovery in GFlowNets. Nikhil Vemgal, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
GFlowNets for Causal Discovery: an Overview. Cristian Dragos Manta, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
Thompson sampling for improved exploration in GFlowNets. Jarrid Rector-Brooks, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
BatchGFN: Generative Flow Networks for Batch Active Learning. Shreshth A. Malik, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation. Chris Chinenye Emezue, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
Conditional Graph Generation with Graph Principal Flow Network. Tianze Luo, et al. ICML 2023 Structured Probabilistic Inference & Generative Modeling workshop.
Goal-conditioned GFlowNets for Controllable Multi-Objective Molecular Design. Julien Roy, et al. ICML 2023 Deployable Generative AI workshop.
TBD

Preprints

Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic Communication. Christo Kurisummoottil Thomas, et al. 2022.
Improving Generative Flow Networks with Path Regularization. Anh Do, et al. 2022.
Generative Multi-Flow Networks: Centralized, Independent and Conservation. Yinchuan Li, et al. 2022.
Start Small: Training Controllable Game Level Generators without Training Data by Learning at Multiple Sizes. Yahia Zakaria, et al. 2022.
Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes. Mizu Nishikawa-Toomey, et al. 2022.
Distributional GFlowNets with Quantile Flows. Dinghuai Zhang, et al. 2023.
Multi-Fidelity Active Learning with GFlowNets. Alex Hernandez-Garcia, et al. 2023.
Generative Flow Networks: a Markov Chain Perspective. Tristan Deleu, et al. 2023.
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets. Tiago da Silva, et al. 2023.
Meta Generative Flow Networks with Personalization for Task-Specific Adaptation. Xinyuan Ji, et al. 2023.
XInsight: Revealing Model Insights for GNNs with Flow-based Explanations. Eli Laird, et al. 2023.
Local Search GFlowNets. Minsu Kim, et al. 2023.
TBD

Coding

GFlowNet Tutorial. Emmanuel Bengio.
torchgfn. Salem Lahlou, et al.
recursionpharma/gflownet. Emmanuel Bengio.
alexhernandezgarcia/gflownet. Alex Hernandez-Garcia.
TBD