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Causal Discovery from Conditionally Stationary Time Series

Causal Discovery from Conditionally Stationary Time Series

12 October 2021
Carles Balsells-Rodas
Ruibo Tu
Tanmayee Narendra
Yingzhen Li
Gabriele Schweikert
Hedvig Kjellström
Yingzhen Li
    AI4TS
    BDL
    CML
ArXivPDFHTML

Papers citing "Causal Discovery from Conditionally Stationary Time Series"

40 / 40 papers shown
Title
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
Zachary C. Brown
David Carlson
CML
AI4CE
23
0
0
27 May 2025
TS-CausalNN: Learning Temporal Causal Relations from Non-linear
  Non-stationary Time Series Data
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
Omar Faruque
Sahara Ali
Xue Zheng
Jianwu Wang
AI4TS
BDL
CML
94
1
0
01 Apr 2024
Case Studies of Causal Discovery from IT Monitoring Time Series
Case Studies of Causal Discovery from IT Monitoring Time Series
Ali Aït-Bachir
Charles K. Assaad
Christophe de Bignicourt
Emilie Devijver
Simon Ferreira
Éric Gaussier
Hosein Mohanna
Lei Zan
CML
AI4TS
60
8
0
28 Jul 2023
Inferring dynamic regulatory interaction graphs from time series data
  with perturbations
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar
Sumner Magruder
E. Brouwer
Aarthi Venkat
Frederik Wenkel
Guy Wolf
Smita Krishnaswamy
44
5
0
13 Jun 2023
Evaluating Temporal Observation-Based Causal Discovery Techniques
  Applied to Road Driver Behaviour
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Rhys Howard
Lars Kunze
CML
60
7
0
31 Jan 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
95
1
0
28 Jan 2023
Rhino: Deep Causal Temporal Relationship Learning With History-dependent
  Noise
Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noise
Wenbo Gong
Joel Jennings
Chen Zhang
Nick Pawlowski
AI4TS
CML
40
26
0
26 Oct 2022
SIMPLE: A Gradient Estimator for $k$-Subset Sampling
SIMPLE: A Gradient Estimator for kkk-Subset Sampling
Kareem Ahmed
Zhe Zeng
Mathias Niepert
Guy Van den Broeck
BDL
79
27
0
04 Oct 2022
GLIDE: Towards Photorealistic Image Generation and Editing with
  Text-Guided Diffusion Models
GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
Alex Nichol
Prafulla Dhariwal
Aditya A. Ramesh
Pranav Shyam
Pamela Mishkin
Bob McGrew
Ilya Sutskever
Mark Chen
302
3,582
0
20 Dec 2021
Deep Explicit Duration Switching Models for Time Series
Deep Explicit Duration Switching Models for Time Series
Abdul Fatir Ansari
Konstantinos Benidis
Richard Kurle
Ali Caner Turkmen
Harold Soh
Alex Smola
Yuyang Wang
Tim Januschowski
BDL
71
20
0
26 Oct 2021
Learning Temporally Causal Latent Processes from General Temporal Data
Learning Temporally Causal Latent Processes from General Temporal Data
Weiran Yao
Yuewen Sun
Alex Ho
Changyin Sun
Kun Zhang
BDL
CML
59
87
0
11 Oct 2021
Neural Relational Inference with Efficient Message Passing Mechanisms
Neural Relational Inference with Efficient Message Passing Mechanisms
Siyuan Chen
Jiahai Wang
Guoqing Li
77
20
0
23 Jan 2021
Counterfactual Generative Networks
Counterfactual Generative Networks
Axel Sauer
Andreas Geiger
OOD
BDL
CML
82
126
0
15 Jan 2021
Causal Discovery in Physical Systems from Videos
Causal Discovery in Physical Systems from Videos
Yunzhu Li
Antonio Torralba
Anima Anandkumar
Dieter Fox
Animesh Garg
CML
98
104
0
01 Jul 2020
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary
  Time Series
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series
Hermanni Hälvä
Aapo Hyvarinen
OOD
BDL
CML
41
79
0
22 Jun 2020
Amortized Causal Discovery: Learning to Infer Causal Graphs from
  Time-Series Data
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
Sindy Löwe
David Madras
R. Zemel
Max Welling
CML
BDL
AI4TS
93
131
0
18 Jun 2020
Language Models are Few-Shot Learners
Language Models are Few-Shot Learners
Tom B. Brown
Benjamin Mann
Nick Ryder
Melanie Subbiah
Jared Kaplan
...
Christopher Berner
Sam McCandlish
Alec Radford
Ilya Sutskever
Dario Amodei
BDL
708
41,894
0
28 May 2020
DYNOTEARS: Structure Learning from Time-Series Data
DYNOTEARS: Structure Learning from Time-Series Data
Roxana Pamfil
Nisara Sriwattanaworachai
Shaan Desai
Philip Pilgerstorfer
Paul Beaumont
K. Georgatzis
Bryon Aragam
CML
AI4TS
BDL
64
191
0
02 Feb 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke
Sam Gross
Francisco Massa
Adam Lerer
James Bradbury
...
Sasank Chilamkurthy
Benoit Steiner
Lu Fang
Junjie Bai
Soumith Chintala
ODL
406
42,393
0
03 Dec 2019
CATER: A diagnostic dataset for Compositional Actions and TEmporal
  Reasoning
CATER: A diagnostic dataset for Compositional Actions and TEmporal Reasoning
Rohit Girdhar
Deva Ramanan
52
178
0
10 Oct 2019
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
Kexin Yi
Yuta Saito
Yunzhu Li
Pushmeet Kohli
Jiajun Wu
Antonio Torralba
J. Tenenbaum
NAI
106
473
0
03 Oct 2019
Causal Discovery and Forecasting in Nonstationary Environments with
  State-Space Models
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
Erdun Gao
Kun Zhang
Biwei Huang
Clark Glymour
CML
AI4TS
53
64
0
26 May 2019
DAG-GNN: DAG Structure Learning with Graph Neural Networks
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu
Jie Chen
Tian Gao
Mo Yu
BDL
CML
GNN
69
486
0
22 Apr 2019
Causal Discovery with General Non-Linear Relationships Using Non-Linear
  ICA
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA
R. Monti
Kun Zhang
Aapo Hyvarinen
CML
62
92
0
19 Apr 2019
Causal Discovery from Heterogeneous/Nonstationary Data with Independent
  Changes
Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes
Erdun Gao
Kun Zhang
Jiji Zhang
Joseph Ramsey
Ruben Sanchez-Romero
Clark Glymour
Bernhard Schölkopf
52
226
0
05 Mar 2019
Causal Discovery in the Presence of Missing Data
Causal Discovery in the Presence of Missing Data
Ruibo Tu
Cheng Zhang
P. Ackermann
Bo Christer Bertilson
Clark Glymour
Hedvig Kjellström
Kun Zhang
CML
52
65
0
11 Jul 2018
Multiple Causal Inference with Latent Confounding
Multiple Causal Inference with Latent Confounding
Rajesh Ranganath
A. Perotte
CML
33
50
0
21 May 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng
Bryon Aragam
Pradeep Ravikumar
Eric Xing
NoLa
CML
OffRL
87
941
0
04 Mar 2018
Causal Discovery in the Presence of Measurement Error: Identifiability
  Conditions
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Kun Zhang
Biwei Huang
Joseph Ramsey
Kayhan Batmanghelich
Peter Spirtes
Clark Glymour
CML
55
30
0
10 Jun 2017
A Structured Self-attentive Sentence Embedding
A Structured Self-attentive Sentence Embedding
Zhouhan Lin
Minwei Feng
Cicero Nogueira dos Santos
Mo Yu
Bing Xiang
Bowen Zhou
Yoshua Bengio
113
2,138
0
09 Mar 2017
Categorical Reparameterization with Gumbel-Softmax
Categorical Reparameterization with Gumbel-Softmax
Eric Jang
S. Gu
Ben Poole
BDL
299
5,364
0
03 Nov 2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random
  Variables
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
Chris J. Maddison
A. Mnih
Yee Whye Teh
BDL
173
2,529
0
02 Nov 2016
Unsupervised Feature Extraction by Time-Contrastive Learning and
  Nonlinear ICA
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Aapo Hyvarinen
H. Morioka
CML
OOD
AI4TS
61
409
0
20 May 2016
Fast and Accurate Deep Network Learning by Exponential Linear Units
  (ELUs)
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Djork-Arné Clevert
Thomas Unterthiner
Sepp Hochreiter
293
5,521
0
23 Nov 2015
A Recurrent Latent Variable Model for Sequential Data
A Recurrent Latent Variable Model for Sequential Data
Junyoung Chung
Kyle Kastner
Laurent Dinh
Kratarth Goel
Aaron Courville
Yoshua Bengio
DRL
BDL
84
1,258
0
07 Jun 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
1.7K
150,006
0
22 Dec 2014
Auto-Encoding Variational Bayes
Auto-Encoding Variational Bayes
Diederik P. Kingma
Max Welling
BDL
435
16,944
0
20 Dec 2013
Causal Discovery with Continuous Additive Noise Models
Causal Discovery with Continuous Additive Noise Models
Jonas Peters
Joris Mooij
Dominik Janzing
Bernhard Schölkopf
CML
100
569
0
26 Sep 2013
On the Identifiability of the Post-Nonlinear Causal Model
On the Identifiability of the Post-Nonlinear Causal Model
Kun Zhang
Aapo Hyvarinen
CML
192
564
0
09 May 2012
Identifiability of Causal Graphs using Functional Models
Identifiability of Causal Graphs using Functional Models
J. Peters
Joris Mooij
Dominik Janzing
Bernhard Schölkopf
86
155
0
14 Feb 2012
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