v1v2 (latest)

Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

30 May 2023

T. Deleu

Mizu Nishikawa-Toomey

Jithendaraa Subramanian

Nikolay Malkin

Papers citing "Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network"

20 / 20 papers shown

Title
Causal Discovery via Bayesian Optimization Bao Duong Sunil Gupta Thin Nguyen 147 0 0 28 Jan 2025
Adaptive teachers for amortized samplers Minsu Kim Sanghyeok Choi Taeyoung Yun Emmanuel Bengio Leo Feng Jarrid Rector-Brooks Sungsoo Ahn Jinkyoo Park Nikolay Malkin Yoshua Bengio 473 7 0 02 Oct 2024
Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets Haoran He C. Chang Huazhe Xu Ling Pan 174 7 0 03 Jun 2024
Investigating Generalization Behaviours of Generative Flow Networks Lazar Atanackovic Emmanuel Bengio AI4CE 69 4 0 07 Feb 2024
GFlowNet-EM for learning compositional latent variable models J. E. Hu Nikolay Malkin Moksh Jain Katie Everett Alexandros Graikos Yoshua Bengio CoGe 89 40 0 13 Feb 2023
Better Training of GFlowNets with Local Credit and Incomplete Trajectories L. Pan Nikolay Malkin Dinghuai Zhang Yoshua Bengio 92 71 0 03 Feb 2023
GFlowNets for AI-Driven Scientific Discovery Moksh Jain T. Deleu Jason S. Hartford Cheng-Hao Liu Alex Hernandez-Garcia Yoshua Bengio AI4CE 78 55 0 01 Feb 2023
NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning Muralikrishnna G. Sethuraman Romain Lopez Ramkumar Veppathur Mohan Faramarz Fekri Tommaso Biancalani Jan-Christian Hütter CML 71 12 0 04 Jan 2023
A Variational Perspective on Generative Flow Networks Heiko Zimmermann Fredrik Lindsten Jan-Willem van de Meent C. A. Naesseth 84 35 0 14 Oct 2022
GFlowNets and variational inference Nikolay Malkin Salem Lahlou T. Deleu Xu Ji J. E. Hu Katie Everett Dinghuai Zhang Yoshua Bengio BDL 198 88 0 02 Oct 2022
Amortized Inference for Causal Structure Learning Lars Lorch Scott Sussex Jonas Rothfuss Andreas Krause Bernhard Schölkopf CML 110 65 0 25 May 2022
Differentiable DAG Sampling Bertrand Charpentier Simon Kibler Stephan Günnemann 91 42 0 16 Mar 2022
Bayesian Structure Learning with Generative Flow Networks T. Deleu António Góis Chris C. Emezue M. Rankawat Simon Lacoste-Julien Stefan Bauer Yoshua Bengio BDL 98 156 0 28 Feb 2022
Variational Causal Networks: Approximate Bayesian Inference over Causal Structures Yashas Annadani Jonas Rothfuss Alexandre Lacoste Nino Scherrer Anirudh Goyal Yoshua Bengio Stefan Bauer BDL CML 79 48 0 14 Jun 2021
Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation Emmanuel Bengio Moksh Jain Maksym Korablyov Doina Precup Yoshua Bengio 103 336 0 08 Jun 2021
DiBS: Differentiable Bayesian Structure Learning Lars Lorch Jonas Rothfuss Bernhard Schölkopf Andreas Krause 67 90 0 25 May 2021
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks Julius von Kügelgen Paul Kishan Rubenstein Bernhard Schölkopf Adrian Weller CML 57 20 0 09 Oct 2019
Relational inductive biases, deep learning, and graph networks Peter W. Battaglia Jessica B. Hamrick V. Bapst Alvaro Sanchez-Gonzalez V. Zambaldi ... Pushmeet Kohli M. Botvinick Oriol Vinyals Yujia Li Razvan Pascanu AI4CE NAI 769 3,131 0 04 Jun 2018
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 90 1,094 0 16 Aug 2016
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data David Heckerman D. Geiger D. M. Chickering TPM 119 3,984 0 27 Feb 2013