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2001.11940
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Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
International Conference on Machine Learning (ICML), 2020
31 January 2020
Basil Saeed
Snigdha Panigrahi
Caroline Uhler
CML
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Papers citing
"Causal Structure Discovery from Distributions Arising from Mixtures of DAGs"
19 / 19 papers shown
Towards Generalization of Graph Neural Networks for AC Optimal Power Flow
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152
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08 Oct 2025
Learning Causal Structure Distributions for Robust Planning
IEEE Robotics and Automation Letters (IEEE RA-L), 2025
Alejandro Murillo-Gonzalez
Junhong Xu
Lantao Liu
CML
252
1
0
08 Aug 2025
Causal Temporal Regime Structure Learning
International Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Abdellah Rahmani
Pascal Frossard
CML
618
2
0
20 Feb 2025
Covariate Dependent Mixture of Bayesian Networks
Román Marchant
Dario Draca
Gilad Francis
Sahand Assadzadeh
Mathew Varidel
Frank Iorfino
Sally Cripps
CML
238
3
0
10 Jan 2025
Learning Mixtures of Unknown Causal Interventions
Neural Information Processing Systems (NeurIPS), 2024
Abhinav Kumar
Kirankumar Shiragur
Caroline Uhler
CML
217
1
0
31 Oct 2024
Causal Modeling in Multi-Context Systems: Distinguishing Multiple Context-Specific Causal Graphs which Account for Observational Support
Martin Rabel
Wiebke Günther
Jakob Runge
Andreas Gerhardus
273
1
0
27 Oct 2024
Interventional Causal Discovery in a Mixture of DAGs
Burak Varıcı
Dmitriy A. Katz-Rogozhnikov
Dennis L. Wei
P. Sattigeri
A. Tajer
CML
308
4
0
12 Jun 2024
Federated Causal Discovery from Heterogeneous Data
Loka Li
Ignavier Ng
Gongxu Luo
Erdun Gao
Guan-Hong Chen
Tongliang Liu
Bin Gu
Kun Zhang
FedML
335
16
0
20 Feb 2024
Causal Discovery under Latent Class Confounding
Bijan Mazaheri
Spencer Gordon
Y. Rabani
Leonard J. Schulman
CML
416
4
0
13 Nov 2023
Discovering Mixtures of Structural Causal Models from Time Series Data
International Conference on Machine Learning (ICML), 2023
Sumanth Varambally
Yi-An Ma
Rose Yu
472
11
0
10 Oct 2023
iSCAN: Identifying Causal Mechanism Shifts among Nonlinear Additive Noise Models
Neural Information Processing Systems (NeurIPS), 2023
Tianyu Chen
Kevin Bello
Bryon Aragam
Pradeep Ravikumar
CML
392
5
0
30 Jun 2023
Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation
International Conference on Machine Learning (ICML), 2023
Wanpeng Zhang
Yilin Li
Boyu Yang
Zongqing Lu
CML
364
4
0
05 Jun 2023
Disentangling Mixtures of Unknown Causal Interventions
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Abhinav Kumar
Gaurav Sinha
CML
212
7
0
01 Oct 2022
Causal Structure Learning: a Combinatorial Perspective
Foundations of Computational Mathematics (FoCM), 2022
C. Squires
Caroline Uhler
CML
543
66
0
02 Jun 2022
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments
Wei Chen
Yunjin Wu
Ruichu Cai
Yueguo Chen
Zijian Li
CML
238
4
0
18 Nov 2021
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters
Ben Lengerich
Caleb N. Ellington
Bryon Aragam
Eric Xing
Manolis Kellis
CML
204
6
0
01 Nov 2021
Fine-Grained System Identification of Nonlinear Neural Circuits
Knowledge Discovery and Data Mining (KDD), 2021
Dawna Bagherian
James Gornet
Jeremy Bernstein
Yu-Li Ni
Yisong Yue
M. Meister
182
3
0
09 Jun 2021
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
CLEaR (CLEaR), 2021
Alex Markham
Richeek Das
Moritz Grosse-Wentrup
249
14
0
07 Jun 2021
Collaborative Causal Discovery with Atomic Interventions
Neural Information Processing Systems (NeurIPS), 2021
Raghavendra Addanki
S. Kasiviswanathan
253
5
0
06 Jun 2021
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