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Towards Identifiability of Interventional Stochastic Differential Equations

21 May 2025

Papers citing "Towards Identifiability of Interventional Stochastic Differential Equations"

24 / 24 papers shown

Title
Interpretable Neural ODEs for Gene Regulatory Network Discovery under Perturbations Zaikang Lin Sei Chang Aaron Zweig Elham Azizi David A. Knowles David A. Knowles 134 1 0 05 Jan 2025
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots Vincent Guan Joseph Janssen Hossein Rahmani Andrew Warren Stephen X. Zhang Elina Robeva Geoffrey Schiebinger DiffM 138 7 0 30 Oct 2024
Causal Modeling with Stationary Diffusions Lars Lorch Andreas Krause Bernhard Schölkopf DiffM 139 13 0 26 Oct 2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions Jiaqi Zhang C. Squires Kristjan Greenewald Akash Srivastava Karthikeyan Shanmugam Caroline Uhler CML 129 65 0 12 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 110 65 0 04 Jun 2023
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets Lazar Atanackovic Alexander Tong Bo Wang Leo J. Lee Yoshua Bengio Jason S. Hartford BDL 103 25 0 08 Feb 2023
Linear Causal Disentanglement via Interventions C. Squires A. Seigal Salil Bhate Caroline Uhler CML 130 68 0 29 Nov 2022
Active Learning for Optimal Intervention Design in Causal Models Jiaqi Zhang Louis V. Cammarata C. Squires T. Sapsis Caroline Uhler CML 109 28 0 10 Sep 2022
Identifiability in Continuous Lyapunov Models Philipp Dettling R. Homs Carlos Améndola Mathias Drton N. Hansen 81 9 0 08 Sep 2022
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 104 141 0 21 Jul 2021
Neural SDEs as Infinite-Dimensional GANs Patrick Kidger James Foster Xuechen Li Harald Oberhauser Terry Lyons DiffM 66 154 0 06 Feb 2021
Maximum Likelihood Training of Score-Based Diffusion Models Yang Song Conor Durkan Iain Murray Stefano Ermon DiffM 222 676 0 22 Jan 2021
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 99 191 0 03 Jul 2020
On Low Rank Directed Acyclic Graphs and Causal Structure Learning Zhuangyan Fang Shengyu Zhu Jiji Zhang Yue Liu Zhitang Chen Yangbo He CML 89 28 0 10 Jun 2020
Scaling structural learning with NO-BEARS to infer causal transcriptome networks Hao-Chih Lee M. Danieletto Riccardo Miotto S. Cherng J. Dudley CML 82 47 0 31 Oct 2019
Learning Activation Functions: A new paradigm for understanding Neural Networks Mohit Goyal R. Goyal Brejesh Lall 72 66 0 23 Jun 2019
Minimum Stein Discrepancy Estimators Alessandro Barp François‐Xavier Briol Andrew B. Duncan Mark Girolami Lester W. Mackey 80 93 0 19 Jun 2019
A simple and efficient architecture for trainable activation functions Andrea Apicella Francesco Isgrò R. Prevete 56 37 0 08 Feb 2019
Interpolating between Optimal Transport and MMD using Sinkhorn Divergences Jean Feydy Thibault Séjourné François-Xavier Vialard S. Amari A. Trouvé Gabriel Peyré OT 100 533 0 18 Oct 2018
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 111 952 0 04 Mar 2018
A Complete Recipe for Stochastic Gradient MCMC Yian Ma Tianqi Chen E. Fox BDL SyDa 124 490 0 15 Jun 2015
Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances Marco Cuturi OT 227 4,303 0 04 Jun 2013
Identifiability of Gaussian structural equation models with equal error variances J. Peters Peter Buhlmann CML 211 339 0 11 May 2012
Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting J. Saunderson V. Chandrasekaran P. Parrilo A. Willsky 100 79 0 05 Apr 2012