DiBS: Differentiable Bayesian Structure Learning

被引:0
|
作者
Lorch, Lars [1 ]
Rothfuss, Jonas [1 ]
Schoelkopf, Bernhard [2 ]
Krause, Andreas [1 ]
机构
[1] Swiss Fed Inst Technol, Zurich, Switzerland
[2] MPI Intelligent Syst, Tubingen, Germany
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) | 2021年 / 34卷
基金
瑞士国家科学基金会; 欧洲研究理事会;
关键词
MARKOV EQUIVALENCE CLASSES; GRAPHICAL MODELS; STRUCTURE DISCOVERY; NETWORK STRUCTURE;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty-a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our formulation directly applicable to posterior inference of complex Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. Using DiBS, we devise an efficient, general purpose variational inference method for approximating distributions over structural models. In evaluations on simulated and real-world data, our method significantly outperforms related approaches to joint posterior inference.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Differentiable Bayesian Structure Learning with Acyclicity Assurance
    Tran, Quang-Duy
    Phuoc Nguyen
    Bao Duong
    Thin Nguyen
    23RD IEEE INTERNATIONAL CONFERENCE ON DATA MINING, ICDM 2023, 2023, : 598 - 607
  • [2] Differentiable TAN Structure Learning for Bayesian Network Classifiers
    Roth, Wolfgang
    Pernkopf, Franz
    INTERNATIONAL CONFERENCE ON PROBABILISTIC GRAPHICAL MODELS, VOL 138, 2020, 138 : 389 - 400
  • [3] Constraining acyclicity of differentiable Bayesian structure learning with topological ordering
    Tran, Quang-Duy
    Nguyen, Phuoc
    Duong, Bao
    Nguyen, Thin
    KNOWLEDGE AND INFORMATION SYSTEMS, 2024, 66 (09) : 5605 - 5630
  • [4] Differentiable and Transportable Structure Learning
    Berrevoets, Jeroen
    Seedat, Nabeel
    Imrie, Fergus
    van der Schaar, Mihaela
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202, 2023, 202
  • [5] BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule
    Zhang, Miao
    Pan, Shirui
    Chang, Xiaojun
    Su, Steven
    Hu, Jilin
    Haffari, Gholamreza
    Yang, Bin
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2022, : 11861 - 11870
  • [6] Graph Differentiable Architecture Search with Structure Learning
    Qin, Yijian
    Wang, Xin
    Zhang, Zeyang
    Zhu, Wenwu
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021, 34
  • [7] End-to-End Differentiable Learning of Protein Structure
    AlQuraishi, Mohammed
    CELL SYSTEMS, 2019, 8 (04) : 292 - +
  • [8] Bayesian Structure Learning by Recursive Bootstrap
    Rohekar, Raanan Y.
    Gurwicz, Yaniv
    Nisimov, Shami
    Koren, Guy
    Novik, Gal
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018), 2018, 31
  • [9] Distributed structure learning of Bayesian networks
    Huang, Hao
    Huang, Jianqing
    Journal of Computational Information Systems, 2007, 3 (04): : 1739 - 1746
  • [10] Parallel Bayesian Network Structure Learning
    Gao, Tian
    Wei, Dennis
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 80, 2018, 80