ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2006.10833
  4. Cited By
Amortized Causal Discovery: Learning to Infer Causal Graphs from
  Time-Series Data

Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data

18 June 2020
Sindy Lowe
David Madras
R. Zemel
Max Welling
    CML
    BDL
    AI4TS
ArXivPDFHTML

Papers citing "Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data"

26 / 26 papers shown
Title
Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning
Albert Wilcox
Mohamed Ghanem
Masoud Moghani
Pierre Barroso
Benjamin Joffe
Animesh Garg
47
0
0
06 Mar 2025
Causal Temporal Regime Structure Learning
Causal Temporal Regime Structure Learning
Abdellah Rahmani
Pascal Frossard
CML
75
2
0
20 Feb 2025
SSL Framework for Causal Inconsistency between Structures and Representations
SSL Framework for Causal Inconsistency between Structures and Representations
Hang Chen
Xinyu Yang
Keqing Du
Wenya Wang
50
2
0
03 Jan 2025
Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving
Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving
Ehsan Ahmadi
Ray Coden Mercurius
Soheil Alizadeh
K. Rezaee
Amir Rasouli
AAML
36
0
0
23 Sep 2024
Learning production functions for supply chains with graph neural networks
Learning production functions for supply chains with graph neural networks
Serina Chang
Zhiyin Lin
Benjamin Yan
Swapnil Bembde
Qi Xiu
...
Yu Qin
Frank Kloster
Alex Luo
Raj Palleti
J. Leskovec
GNN
AI4TS
39
1
0
26 Jul 2024
Demystifying amortized causal discovery with transformers
Demystifying amortized causal discovery with transformers
Francesco Montagna
Max Cairney-Leeming
Dhanya Sridhar
Francesco Locatello
CML
57
1
0
27 May 2024
Learning Independently from Causality in Multi-Agent Environments
Learning Independently from Causality in Multi-Agent Environments
Rafael Pina
V. D. Silva
Corentin Artaud
LRM
14
0
0
05 Nov 2023
Neural Relational Inference with Fast Modular Meta-learning
Neural Relational Inference with Fast Modular Meta-learning
Ferran Alet
Erica Weng
Tomás Lozano Pérez
L. Kaelbling
45
55
0
10 Oct 2023
Discovering Mixtures of Structural Causal Models from Time Series Data
Discovering Mixtures of Structural Causal Models from Time Series Data
Sumanth Varambally
Yi-An Ma
Rose Yu
20
4
0
10 Oct 2023
Learning a Structural Causal Model for Intuition Reasoning in
  Conversation
Learning a Structural Causal Model for Intuition Reasoning in Conversation
Hang Chen
Bingyu Liao
Jing Luo
Wenjing Zhu
Xinyu Yang
LRM
25
12
0
28 May 2023
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Yuxiao Cheng
Lianglong Li
Tingxiong Xiao
Zongren Li
Qionghai Dai
J. Suo
K. He
CML
BDL
AI4TS
21
21
0
10 May 2023
Causal Semantic Communication for Digital Twins: A Generalizable
  Imitation Learning Approach
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach
Christo Kurisummoottil Thomas
Walid Saad
Yong Xiao
27
20
0
25 Apr 2023
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
Uzma Hasan
Emam Hossain
Md. Osman Gani
CML
AI4TS
23
24
0
27 Mar 2023
Causality Detection for Efficient Multi-Agent Reinforcement Learning
Causality Detection for Efficient Multi-Agent Reinforcement Learning
Rafael Pina
V. D. Silva
Corentin Artaud
LRM
8
0
0
24 Mar 2023
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
GDBN: a Graph Neural Network Approach to Dynamic Bayesian Network
Yang Sun
Yifan Xie
BDL
CML
21
1
0
28 Jan 2023
Inversion of Bayesian Networks
Inversion of Bayesian Networks
Jesse van Oostrum
Peter van Hintum
Nihat Ay
BDL
8
1
0
20 Dec 2022
Deep Causal Learning: Representation, Discovery and Inference
Deep Causal Learning: Representation, Discovery and Inference
Zizhen Deng
Xiaolong Zheng
Hu Tian
D. Zeng
CML
BDL
26
11
0
07 Nov 2022
DIDER: Discovering Interpretable Dynamically Evolving Relations
DIDER: Discovering Interpretable Dynamically Evolving Relations
Enna Sachdeva
Chiho Choi
21
2
0
22 Aug 2022
Amortized Inference for Causal Structure Learning
Amortized Inference for Causal Structure Learning
Lars Lorch
Scott Sussex
Jonas Rothfuss
Andreas Krause
Bernhard Schölkopf
CML
11
60
0
25 May 2022
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
St John Grimbly
Jonathan P. Shock
Arnu Pretorius
44
17
0
12 Nov 2021
Causal Discovery from Conditionally Stationary Time Series
Causal Discovery from Conditionally Stationary Time Series
Carles Balsells-Rodas
Ruibo Tu
Hedvig Kjellström
Yingzhen Li
Gabriele Schweikert
Hedvig Kjellstrom
Yingzhen Li
BDL
CML
AI4TS
32
5
0
12 Oct 2021
Relating Graph Neural Networks to Structural Causal Models
Relating Graph Neural Networks to Structural Causal Models
Matej Zečević
D. Dhami
Petar Velickovic
Kristian Kersting
AI4CE
CML
55
53
0
09 Sep 2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep
  learning representations with expert knowledge graphs: the MonuMAI cultural
  heritage use case
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
Natalia Díaz Rodríguez
Alberto Lamas
Jules Sanchez
Gianni Franchi
Ivan Donadello
S. Tabik
David Filliat
P. Cruz
Rosana Montes
Francisco Herrera
45
77
0
24 Apr 2021
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
M. Vowels
Necati Cihan Camgöz
Richard Bowden
CML
20
296
0
03 Mar 2021
Learning Sparse Nonparametric DAGs
Learning Sparse Nonparametric DAGs
Xun Zheng
Chen Dan
Bryon Aragam
Pradeep Ravikumar
Eric P. Xing
CML
103
258
0
29 Sep 2019
Interaction Networks for Learning about Objects, Relations and Physics
Interaction Networks for Learning about Objects, Relations and Physics
Peter W. Battaglia
Razvan Pascanu
Matthew Lai
Danilo Jimenez Rezende
Koray Kavukcuoglu
AI4CE
OCL
PINN
GNN
260
1,400
0
01 Dec 2016
1