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Variational Autoencoders and Nonlinear ICA: A Unifying Framework
v1v2v3v4 (latest)

Variational Autoencoders and Nonlinear ICA: A Unifying Framework

International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
10 July 2019
Ilyes Khemakhem
Diederik P. Kingma
Ricardo Pio Monti
Aapo Hyvarinen
    OOD
ArXiv (abs)PDFHTML

Papers citing "Variational Autoencoders and Nonlinear ICA: A Unifying Framework"

50 / 402 papers shown
Training Dynamics of Nonlinear Contrastive Learning Model in the High
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Identifying latent state transition in non-linear dynamical systems
Identifying latent state transition in non-linear dynamical systems
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Pekka Marttinen
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161
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Learning Discrete Concepts in Latent Hierarchical Models
Learning Discrete Concepts in Latent Hierarchical Models
Lingjing Kong
Guan-Hong Chen
Erdun Gao
Eric P. Xing
Yuejie Chi
Kun Zhang
409
12
0
01 Jun 2024
Sparsity regularization via tree-structured environments for
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Sparsity regularization via tree-structured environments for disentangled representations
Elliot Layne
Jason S. Hartford
Sébastien Lachapelle
Mathieu Blanchette
Dhanya Sridhar
OODCML
225
1
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30 May 2024
Identifiability of a statistical model with two latent vectors:
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Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding
Hiroaki Sasaki
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184
0
0
30 May 2024
CAVACHON: a hierarchical variational autoencoder to integrate
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CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data
Ping-Han Hsieh
Ru-Xiu Hsiao
Katalin Ferenc
Anthony Mathelier
R. Burkholz
Chien-Yu Chen
G. K. Sandve
T. Belova
M. Kuijjer
129
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0
28 May 2024
From Orthogonality to Dependency: Learning Disentangled Representation
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From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
Ruichu Cai
Zhifan Jiang
Zijian Li
Weilin Chen
Xuexin Chen
Zhifeng Hao
Yifan Shen
Guan-Hong Chen
Kun Zhang
316
3
0
25 May 2024
Structural Disentanglement of Causal and Correlated Concepts
Structural Disentanglement of Causal and Correlated Concepts
Qilong Zhao
Shiyu Wang
Zeeshan Memon
Bo Pan
Guangji Bai
Bo Pan
Zhaohui Qin
Liang Zhao
259
1
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On the Identification of Temporally Causal Representation with Instantaneous Dependence
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Yifan Shen
Kaitao Zheng
Ruichu Cai
Xiangchen Song
Biwei Huang
Zhengmao Zhu
Guan-Hong Chen
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622
11
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24 May 2024
Causal Diffusion Autoencoders: Toward Counterfactual Generation via
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Chengli Zhao
Feng Chen
Xintao Wu
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311
19
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Distributional Principal Autoencoders
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Xinwei Shen
N. Meinshausen
232
5
0
21 Apr 2024
Tripod: Three Complementary Inductive Biases for Disentangled
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Kyle Hsu
Jubayer Ibn Hamid
Kaylee Burns
Chelsea Finn
Jiajun Wu
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246
11
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16 Apr 2024
Propensity Score Alignment of Unpaired Multimodal Data
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Johnny Xi
Jason S. Hartford
195
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0
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ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object
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Daniel Winter
Matan Cohen
Shlomi Fruchter
Yael Pritch
Alex Rav-Acha
Yedid Hoshen
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185
54
0
27 Mar 2024
Identifiable Latent Neural Causal Models
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Yuhang Liu
Zhen Zhang
Dong Gong
Biwei Huang
Erdun Gao
Anton Van Den Hengel
Kun Zhang
Javen Qinfeng Shi
CMLOOD
318
10
0
23 Mar 2024
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Non-negative Contrastive Learning
Yifei Wang
Tao Gui
Yaoyu Guo
Yisen Wang
337
14
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CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for
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CASPER: Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series ImputationInternational Conference on Information and Knowledge Management (CIKM), 2024
Baoyu Jing
Dawei Zhou
Kan Ren
Carl Yang
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351
18
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18 Mar 2024
Towards the Reusability and Compositionality of Causal Representations
Towards the Reusability and Compositionality of Causal RepresentationsCLEaR (CLEaR), 2024
Davide Talon
Phillip Lippe
Stuart James
Alessio Del Bue
Sara Magliacane
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231
7
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14 Mar 2024
Towards Model-Agnostic Posterior Approximation for Fast and Accurate
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Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders
Yaniv Yacoby
Weiwei Pan
Finale Doshi-Velez
DRL
389
0
0
13 Mar 2024
A Sparsity Principle for Partially Observable Causal Representation
  Learning
A Sparsity Principle for Partially Observable Causal Representation LearningInternational Conference on Machine Learning (ICML), 2024
Danru Xu
Dingling Yao
Sébastien Lachapelle
Perouz Taslakian
Julius von Kügelgen
Francesco Locatello
Sara Magliacane
CML
275
22
0
13 Mar 2024
Algorithmic Identification of Essential Exogenous Nodes for Causal
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Algorithmic Identification of Essential Exogenous Nodes for Causal Sufficiency in Brain Networks
Abdolmahdi Bagheri
Mahdi Dehshiri
Babak N. Araabi
Alireza Akhondi-Asl
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312
1
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08 Mar 2024
Why Online Reinforcement Learning is Causal
Why Online Reinforcement Learning is Causal
Oliver Schulte
Pascal Poupart
CMLOffRL
292
2
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On the Origins of Linear Representations in Large Language Models
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Yibo Jiang
Goutham Rajendran
Pradeep Ravikumar
Bryon Aragam
Victor Veitch
253
48
0
06 Mar 2024
Towards Controllable Time Series Generation
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Yifan Bao
Yihao Ang
Qiang Huang
Anthony K. H. Tung
Zhiyong Huang
DiffM
351
6
0
06 Mar 2024
Counterfactual Generation with Identifiability Guarantees
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Hanqi Yan
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Lin Gui
Yuejie Chi
Eric P. Xing
Yulan He
Kun Zhang
CMLOOD
244
10
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23 Feb 2024
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Zijian Li
Ruichu Cai
Zhenhui Yang
Haiqin Huang
Guan-Hong Chen
Yifan Shen
Zijian Li
Xiangchen Song
Kun Zhang
OODAI4TS
260
3
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20 Feb 2024
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts
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Emile van Krieken
Antonio Vergari
Baptiste Caramiaux
Stefano Teso
342
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Separating common from salient patterns with Contrastive Representation
  Learning
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Robin Louiset
Edouard Duchesnay
Antoine Grigis
Pietro Gori
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259
3
0
19 Feb 2024
Learning Interpretable Concepts: Unifying Causal Representation Learning
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Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models
Goutham Rajendran
Simon Buchholz
Bryon Aragam
Bernhard Schölkopf
Pradeep Ravikumar
AI4CE
406
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14 Feb 2024
A Unified Causal View of Instruction Tuning
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Luyao Chen
Wei Huang
Ruqing Zhang
Wei Chen
Jiafeng Guo
Xueqi Cheng
175
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Beyond DAGs: A Latent Partial Causal Model for Multimodal Learning
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Zhen Zhang
Dong Gong
Erdun Gao
Biwei Huang
Anton Van Den Hengel
Kun Zhang
Javen Qinfeng Shi
Javen Qinfeng Shi
284
7
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09 Feb 2024
Counterfactual Image Editing
Counterfactual Image Editing
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Elias Bareinboim
BDLCML
275
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Causal Representation Learning from Multiple Distributions: A General
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Kun Zhang
Shaoan Xie
Ignavier Ng
Yujia Zheng
CMLOOD
376
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On Provable Length and Compositional Generalization
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366
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Double InfoGAN for Contrastive Analysis
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Toward the Identifiability of Comparative Deep Generative Models
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CaRiNG: Learning Temporal Causal Representation under Non-Invertible
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258
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Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration
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Predictive variational autoencoder for learning robust representations
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Learning Unknown Intervention Targets in Structural Causal Models from
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301
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Invariance & Causal Representation Learning: Prospects and Limitations
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Identifiable Feature Learning for Spatial Data with Nonlinear ICA
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Learning Causal Representations from General Environments:
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