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FedDAG: Federated DAG Structure Learning

7 December 2021

Li Shen

Papers citing "FedDAG: Federated DAG Structure Learning"

48 / 48 papers shown

Title
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models Erdun Gao Ignavier Ng Biwei Huang Li Shen Wei Huang Tongliang Liu Kun Zhang H. Bondell CML 107 23 0 27 May 2022
Federated Learning with Partial Model Personalization Krishna Pillutla Kshitiz Malik Abdel-rahman Mohamed Michael G. Rabbat Maziar Sanjabi Lin Xiao FedML 83 166 0 08 Apr 2022
Federated Graph Neural Networks: Overview, Techniques and Challenges R. Liu Pengwei Xing Zichao Deng Anran Li Cuntai Guan Han Yu FedML 91 83 0 15 Feb 2022
gCastle: A Python Toolbox for Causal Discovery Keli Zhang Shengyu Zhu Marcus Kalander Ignavier Ng Junjian Ye Zhitang Chen Lujia Pan CML 96 61 0 30 Nov 2021
Towards Federated Bayesian Network Structure Learning with Continuous Optimization Ignavier Ng Kun Zhang FedML 66 38 0 18 Oct 2021
A survey of Bayesian Network structure learning N. K. Kitson Anthony C. Constantinou Zhi-gao Guo Yang Liu Kiattikun Chobtham CML 69 190 0 23 Sep 2021
Learning Neural Causal Models with Active Interventions Nino Scherrer O. Bilaniuk Yashas Annadani Anirudh Goyal Patrick Schwab Bernhard Schölkopf Michael C. Mozer Yoshua Bengio Stefan Bauer Nan Rosemary Ke CML 95 43 0 06 Sep 2021
Federated Graph Classification over Non-IID Graphs Han Xie Jing Ma Li Xiong Carl Yang FedML 76 161 0 25 Jun 2021
DAGs with No Curl: An Efficient DAG Structure Learning Approach Yue Yu Tian Gao Naiyu Yin Q. Ji CML 62 60 0 14 Jun 2021
Ordering-Based Causal Discovery with Reinforcement Learning Xiaoqiang Wang Yali Du Shengyu Zhu Liangjun Ke Zhitang Chen Jianye Hao Jun Wang CML 62 64 0 14 May 2021
FedNLP: Benchmarking Federated Learning Methods for Natural Language Processing Tasks Bill Yuchen Lin Chaoyang He ZiHang Zeng Hulin Wang Yufen Huang Christophe Dupuy Rahul Gupta Mahdi Soltanolkotabi Xiang Ren Salman Avestimehr FedML 62 116 0 18 Apr 2021
Unsuitability of NOTEARS for Causal Graph Discovery Marcus Kaiser Maksim Sipos CML 89 65 0 12 Apr 2021
Exploiting Shared Representations for Personalized Federated Learning Liam Collins Hamed Hassani Aryan Mokhtari Sanjay Shakkottai FedML OOD 101 720 0 14 Feb 2021
On the Convergence of Continuous Constrained Optimization for Structure Learning Ignavier Ng Sébastien Lachapelle Nan Rosemary Ke Simon Lacoste-Julien Kun Zhang 78 38 0 23 Nov 2020
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks Dennis L. Wei Tian Gao Yue Yu CML 80 71 0 18 Oct 2020
Differentiable Causal Discovery Under Unmeasured Confounding Rohit Bhattacharya Tushar Nagarajan Daniel Malinsky I. Shpitser CML 65 60 0 14 Oct 2020
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 64 188 0 03 Jul 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 55 190 0 17 Jun 2020
Robust Federated Learning: The Case of Affine Distribution Shifts Amirhossein Reisizadeh Farzan Farnia Ramtin Pedarsani Ali Jadbabaie FedML OOD 82 164 0 16 Jun 2020
On Low Rank Directed Acyclic Graphs and Causal Structure Learning Zhuangyan Fang Shengyu Zhu Jiji Zhang Yue Liu Zhitang Chen Yangbo He CML 67 28 0 10 Jun 2020
Federated Learning with Matched Averaging Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris Papailiopoulos Y. Khazaeni FedML 121 1,124 0 15 Feb 2020
Domain Adaptation as a Problem of Inference on Graphical Models Kun Zhang Biwei Huang P. Stojanov Erdun Gao Qingsong Liu Clark Glymour OOD 102 65 0 09 Feb 2020
DYNOTEARS: Structure Learning from Time-Series Data Roxana Pamfil Nisara Sriwattanaworachai Shaan Desai Philip Pilgerstorfer Paul Beaumont K. Georgatzis Bryon Aragam CML AI4TS BDL 72 192 0 02 Feb 2020
A Graph Autoencoder Approach to Causal Structure Learning Ignavier Ng Shengyu Zhu Zhitang Chen Zhuangyan Fang BDL CML 54 83 0 18 Nov 2019
Federated Learning with Differential Privacy: Algorithms and Performance Analysis Kang Wei Jun Li Ming Ding Chuan Ma Heng Yang Farokhi Farhad Shi Jin Tony Q.S. Quek H. Vincent Poor FedML 125 1,616 0 01 Nov 2019
On the Convergence of Local Descent Methods in Federated Learning Farzin Haddadpour M. Mahdavi FedML 79 273 0 31 Oct 2019
Masked Gradient-Based Causal Structure Learning Ignavier Ng Shengyu Zhu Zhuangyan Fang Haoyang Li Zhitang Chen Jun Wang CML 112 117 0 18 Oct 2019
Learning Neural Causal Models from Unknown Interventions Nan Rosemary Ke O. Bilaniuk Anirudh Goyal Stefan Bauer Hugo Larochelle Bernhard Schölkopf Michael C. Mozer C. Pal Yoshua Bengio CML OOD 107 169 0 02 Oct 2019
Learning Sparse Nonparametric DAGs Xun Zheng Chen Dan Bryon Aragam Pradeep Ravikumar Eric Xing CML 155 260 0 29 Sep 2019
Invariant Risk Minimization Martín Arjovsky Léon Bottou Ishaan Gulrajani David Lopez-Paz OOD 186 2,229 0 05 Jul 2019
Causal Discovery with Reinforcement Learning Shengyu Zhu Ignavier Ng Zhitang Chen CML 58 240 0 11 Jun 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 59 275 0 05 Jun 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks Yue Yu Jie Chen Tian Gao Mo Yu BDL CML GNN 78 487 0 22 Apr 2019
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes Erdun Gao Kun Zhang Jiji Zhang Joseph Ramsey Ruben Sanchez-Romero Clark Glymour Bernhard Schölkopf 62 229 0 05 Mar 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 75 2,318 0 13 Feb 2019
Federated Optimization in Heterogeneous Networks Tian Li Anit Kumar Sahu Manzil Zaheer Maziar Sanjabi Ameet Talwalkar Virginia Smith FedML 180 5,184 0 14 Dec 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 99 942 0 04 Mar 2018
Detecting and Correcting for Label Shift with Black Box Predictors Zachary Chase Lipton Yu Wang Alex Smola OOD 66 554 0 12 Feb 2018
Categorical Reparameterization with Gumbel-Softmax Eric Jang S. Gu Ben Poole BDL 336 5,364 0 03 Nov 2016
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016
Causal inference using invariant prediction: identification and confidence intervals J. Peters Peter Buhlmann N. Meinshausen OOD 122 971 0 06 Jan 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,115 0 22 Dec 2014
Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets Sofia Triantafillou Ioannis Tsamardinos CML 131 150 0 10 Mar 2014
CAM: Causal additive models, high-dimensional order search and penalized regression Peter Buhlmann J. Peters J. Ernest CML 119 325 0 06 Oct 2013
Causal Discovery from Changes Jin Tian Judea Pearl CML 116 165 0 10 Jan 2013
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 175 338 0 11 May 2012
On the Identifiability of the Post-Nonlinear Causal Model Kun Zhang Aapo Hyvarinen CML 199 565 0 09 May 2012
Identifiability of Causal Graphs using Functional Models J. Peters Joris Mooij Dominik Janzing Bernhard Schölkopf 92 155 0 14 Feb 2012