Marrying Causal Representation Learning with Dynamical Systems for Science

22 May 2024

Dingling Yao

Francesco Locatello

Papers citing "Marrying Causal Representation Learning with Dynamical Systems for Science"

45 / 45 papers shown

Title
Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research Patrik Reizinger Randall Balestriero David Klindt Wieland Brendel 117 0 0 17 Apr 2025
Sanity Checking Causal Representation Learning on a Simple Real-World System Juan L. Gamella Simon Bing Jakob Runge CML 136 1 0 27 Feb 2025
A Sparsity Principle for Partially Observable Causal Representation Learning Danru Xu Dingling Yao Sébastien Lachapelle Perouz Taslakian Julius von Kügelgen Francesco Locatello Sara Magliacane CML 60 14 0 13 Mar 2024
Mechanistic Neural Networks for Scientific Machine Learning Adeel Pervez Francesco Locatello E. Gavves PINN AI4CE 42 9 0 20 Feb 2024
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation Zijian Li Ruichu Cai Zhenhui Yang Haiqin Huang Guan-Hong Chen Yifan Shen Zhengming Chen Xiangchen Song Kun Zhang OOD AI4TS 59 1 0 20 Feb 2024
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis Goutham Rajendran Patrik Reizinger Wieland Brendel Pradeep Ravikumar CML 103 8 0 29 Nov 2023
Multi-View Causal Representation Learning with Partial Observability Dingling Yao Danru Xu Sébastien Lachapelle Sara Magliacane Perouz Taslakian Georg Martius Julius von Kügelgen Francesco Locatello CML 95 36 0 07 Nov 2023
Temporally Disentangled Representation Learning under Unknown Nonstationarity Xiangchen Song Weiran Yao Yewen Fan Xinshuai Dong Guan-Hong Chen Juan Carlos Niebles Eric P. Xing Kun Zhang CML OOD 75 12 0 28 Oct 2023
General Identifiability and Achievability for Causal Representation Learning Burak Varici Emre Acartürk Karthikeyan Shanmugam A. Tajer CML 72 21 0 24 Oct 2023
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers Stéphane d’Ascoli Soren Becker Alexander Mathis Philippe Schwaller Niki Kilbertus 58 23 0 09 Oct 2023
Predicting Ordinary Differential Equations with Transformers Soren Becker M. Klein Alexander Neitz Giambattista Parascandolo Niki Kilbertus 65 15 0 24 Jul 2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions Jiaqi Zhang C. Squires Kristjan Greenewald Akash Srivastava Karthikeyan Shanmugam Caroline Uhler CML 67 61 0 12 Jul 2023
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation Sébastien Lachapelle Divyat Mahajan Ioannis Mitliagkas Simon Lacoste-Julien 58 28 0 05 Jul 2023
Learning Linear Causal Representations from Interventions under General Nonlinear Mixing Simon Buchholz Goutham Rajendran Elan Rosenfeld Bryon Aragam Bernhard Schölkopf Pradeep Ravikumar CML 68 64 0 04 Jun 2023
Nonparametric Identifiability of Causal Representations from Unknown Interventions Julius von Kügelgen M. Besserve Wendong Liang Luigi Gresele Armin Kekić Elias Bareinboim David M. Blei Bernhard Schölkopf CML 82 63 0 01 Jun 2023
On the Identifiability of Switching Dynamical Systems Carles Balsells-Rodas Yixin Wang Yingzhen Li 56 4 0 25 May 2023
Simultaneous identification of models and parameters of scientific simulators Cornelius Schroder Jakob H. Macke 65 4 0 24 May 2023
Identifiability Results for Multimodal Contrastive Learning Imant Daunhawer Alice Bizeul Emanuele Palumbo Alexander Marx Julia E. Vogt 52 41 0 16 Mar 2023
Unpaired Multi-Domain Causal Representation Learning Nils Sturma C. Squires Mathias Drton Caroline Uhler OOD CML 66 20 0 02 Feb 2023
Linear Causal Disentanglement via Interventions C. Squires A. Seigal Salil Bhate Caroline Uhler CML 52 68 0 29 Nov 2022
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning Sébastien Lachapelle T. Deleu Divyat Mahajan Ioannis Mitliagkas Yoshua Bengio Simon Lacoste-Julien Quentin Bertrand 44 33 0 26 Nov 2022
Temporally Disentangled Representation Learning Weiran Yao Guangyi Chen Kun Zhang CML BDL OOD 52 51 0 24 Oct 2022
Interventional Causal Representation Learning Kartik Ahuja Divyat Mahajan Yixin Wang Yoshua Bengio CML 65 93 0 24 Sep 2022
Partial Disentanglement via Mechanism Sparsity Sébastien Lachapelle Simon Lacoste-Julien 44 25 0 15 Jul 2022
Identifiability of deep generative models without auxiliary information Bohdan Kivva Goutham Rajendran Pradeep Ravikumar Bryon Aragam DRL 67 51 0 20 Jun 2022
On Finite-Sample Identifiability of Contrastive Learning-Based Nonlinear Independent Component Analysis Qi Lyu Xiao Fu CML 46 4 0 14 Jun 2022
Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems Phillip Lippe Sara Magliacane Sindy Löwe Yuki M. Asano Taco S. Cohen E. Gavves CML 46 30 0 13 Jun 2022
Weakly Supervised Representation Learning with Sparse Perturbations Kartik Ahuja Jason S. Hartford Yoshua Bengio SSL 65 60 0 02 Jun 2022
Weakly supervised causal representation learning Johann Brehmer P. D. Haan Phillip Lippe Taco S. Cohen OOD CML 57 126 0 30 Mar 2022
CITRIS: Causal Identifiability from Temporal Intervened Sequences Phillip Lippe Sara Magliacane Sindy Löwe Yuki M. Asano Taco S. Cohen E. Gavves CML 55 108 0 07 Feb 2022
Identifiable Deep Generative Models via Sparse Decoding Gemma E. Moran Dhanya Sridhar Yixin Wang David M. Blei BDL 50 48 0 20 Oct 2021
Discovering Sparse Interpretable Dynamics from Partial Observations Peter Y. Lu Joan Ariño Bernad Marin Soljacic AI4CE 37 25 0 22 Jul 2021
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA Sébastien Lachapelle Pau Rodríguez López Yash Sharma Katie Everett Rémi Le Priol Alexandre Lacoste Simon Lacoste-Julien CML OOD 75 138 0 21 Jul 2021
Understanding Latent Correlation-Based Multiview Learning and Self-Supervision: An Identifiability Perspective Qinjie Lyu Xiao Fu Weiran Wang Songtao Lu SSL 29 31 0 14 Jun 2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style Julius von Kügelgen Yash Sharma Luigi Gresele Wieland Brendel Bernhard Schölkopf M. Besserve Francesco Locatello 76 312 0 08 Jun 2021
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 262 217 0 17 Feb 2021
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding David A. Klindt Lukas Schott Yash Sharma Ivan Ustyuzhaninov Wieland Brendel Matthias Bethge Dylan M. Paiton CML 79 132 0 21 Jul 2020
On Second Order Behaviour in Augmented Neural ODEs Alexander Norcliffe Cristian Bodnar Ben Day Nikola Simidjievski Pietro Lio 58 91 0 12 Jun 2020
SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamics Kadierdan Kaheman J. Nathan Kutz Steven L. Brunton 36 266 0 05 Apr 2020
A Survey of Deep Learning for Scientific Discovery M. Raghu Erica Schmidt OOD AI4CE 99 123 0 26 Mar 2020
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 217 316 0 07 Feb 2020
The frontier of simulation-based inference Kyle Cranmer Johann Brehmer Gilles Louppe AI4CE 137 842 0 04 Nov 2019
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations Francesco Locatello Stefan Bauer Mario Lucic Gunnar Rätsch Sylvain Gelly Bernhard Schölkopf Olivier Bachem OOD 107 1,463 0 29 Nov 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 315 5,076 0 19 Jun 2018
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEs Philippe Wenk Alkis Gotovos Stefan Bauer Nico S. Gorbach Andreas Krause J. M. Buhmann BDL 20 48 0 12 Apr 2018