Demystifying amortized causal discovery with transformers

27 May 2024

Francesco Locatello

Papers citing "Demystifying amortized causal discovery with transformers"

32 / 32 papers shown

Title
Shortcuts for causal discovery of nonlinear models by score matching Francesco Montagna Nicoletta Noceti Lorenzo Rosasco Francesco Locatello CML 58 3 0 22 Oct 2023
Assumption violations in causal discovery and the robustness of score matching Francesco Montagna Atalanti A. Mastakouri Elias Eulig Nicoletta Noceti Lorenzo Rosasco Dominik Janzing Bryon Aragam Francesco Locatello OOD 33 17 0 20 Oct 2023
Constraint-Free Structure Learning with Smooth Acyclic Orientations Riccardo Massidda Francesco Landolfi Martina Cinquini Davide Bacciu 45 6 0 15 Sep 2023
Causal Discovery with Score Matching on Additive Models with Arbitrary Noise Francesco Montagna Nicoletta Noceti Lorenzo Rosasco Kun Zhang Francesco Locatello CML 22 27 0 06 Apr 2023
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization Kevin Bello Bryon Aragam Pradeep Ravikumar 78 78 0 16 Sep 2022
Truncated Matrix Power Iteration for Differentiable DAG Learning Zhen Zhang Ignavier Ng Dong Gong Yuhang Liu Ehsan Abbasnejad Biwei Huang Kun Zhang Javen Qinfeng Shi 44 25 0 30 Aug 2022
Amortized Inference for Causal Structure Learning Lars Lorch Scott Sussex Jonas Rothfuss Andreas Krause Bernhard Schölkopf CML 58 60 0 25 May 2022
Score matching enables causal discovery of nonlinear additive noise models Paul Rolland Volkan Cevher Matthäus Kleindessner Chris Russel Bernhard Schölkopf Dominik Janzing Francesco Locatello CML 64 85 0 08 Mar 2022
Optimal transport for causal discovery Ruibo Tu Kun Zhang Hedvig Kjellström Cheng Zhang 107 19 0 23 Jan 2022
ML4C: Seeing Causality Through Latent Vicinity Haoyue Dai Rui Ding Yuanyuan Jiang Shi Han Dongmei Zhang OOD 45 13 0 01 Oct 2021
Typing assumptions improve identification in causal discovery P. Brouillard Perouz Taslakian Alexandre Lacoste Sébastien Lachapelle Alexandre Drouin CML 27 13 0 22 Jul 2021
Efficient Neural Causal Discovery without Acyclicity Constraints Phillip Lippe Taco S. Cohen E. Gavves CML 21 73 0 22 Jul 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning Jannik Kossen Neil Band Clare Lyle Aidan Gomez Tom Rainforth Y. Gal OOD 3DPC 51 138 0 04 Jun 2021
Ordering-Based Causal Discovery with Reinforcement Learning Xiaoqiang Wang Yali Du Shengyu Zhu Liangjun Ke Zhitang Chen Jianye Hao Jun Wang CML 33 64 0 14 May 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Game Alexander G. Reisach C. Seiler S. Weichwald CML 38 138 0 26 Feb 2021
Differentiable Causal Discovery from Interventional Data P. Brouillard Sébastien Lachapelle Alexandre Lacoste Simon Lacoste-Julien Alexandre Drouin CML 29 184 0 03 Jul 2020
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data Sindy Löwe David Madras R. Zemel Max Welling CML BDL AI4TS 66 128 0 18 Jun 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 30 189 0 17 Jun 2020
Supervised Whole DAG Causal Discovery Hebi Li Qi Xiao Jin Tian CML 29 17 0 08 Jun 2020
Shortcut Learning in Deep Neural Networks Robert Geirhos J. Jacobsen Claudio Michaelis R. Zemel Wieland Brendel Matthias Bethge Felix Wichmann 120 2,023 0 16 Apr 2020
Conditional Independence Testing using Generative Adversarial Networks Alexis Bellot M. Schaar CML GAN 35 51 0 09 Jul 2019
Gradient-Based Neural DAG Learning Sébastien Lachapelle P. Brouillard T. Deleu Simon Lacoste-Julien BDL CML 23 270 0 05 Jun 2019
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA R. Monti Kun Zhang Aapo Hyvarinen CML 27 91 0 19 Apr 2019
DAGs with NO TEARS: Continuous Optimization for Structure Learning Xun Zheng Bryon Aragam Pradeep Ravikumar Eric Xing NoLa CML OffRL 50 925 0 04 Mar 2018
Model-Powered Conditional Independence Test Rajat Sen A. Suresh Karthikeyan Shanmugam A. Dimakis Sanjay Shakkottai VLM CML 26 91 0 18 Sep 2017
Attention Is All You Need Ashish Vaswani Noam M. Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan Gomez Lukasz Kaiser Illia Polosukhin 3DV 230 129,831 0 12 Jun 2017
Learning Theory for Distribution Regression Z. Szabó Bharath K. Sriperumbudur Barnabás Póczós Arthur Gretton OOD 28 137 0 08 Nov 2014
CAM: Causal additive models, high-dimensional order search and penalized regression Peter Buhlmann J. Peters J. Ernest CML 81 323 0 06 Oct 2013
Causal Discovery with Continuous Additive Noise Models Jonas Peters Joris Mooij Dominik Janzing Bernhard Schölkopf CML 57 563 0 26 Sep 2013
Geometry of the faithfulness assumption in causal inference Caroline Uhler Garvesh Raskutti Peter Buhlmann B. Yu 80 220 0 02 Jul 2012
On the Identifiability of the Post-Nonlinear Causal Model Kun Zhang Aapo Hyvarinen CML 110 558 0 09 May 2012
DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model Shohei Shimizu Takanori Inazumi Yasuhiro Sogawa Aapo Hyvarinen Yoshinobu Kawahara Takashi Washio P. Hoyer K. Bollen CML 56 503 0 13 Jan 2011