From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling

17 October 2023

Papers citing "From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling"

6 / 6 papers shown

Title
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models Aneesh Komanduri Chengli Zhao Feng Chen Xintao Wu CML DiffM 30 4 0 27 Apr 2024
On the Challenges and Opportunities in Generative AI Laura Manduchi Kushagra Pandey Robert Bamler Ryan Cotterell Sina Daubener ... F. Wenzel Frank Wood Stephan Mandt Vincent Fortuin Vincent Fortuin 56 17 0 28 Feb 2024
Counterfactual Generation Under Confounding Abbavaram Gowtham Reddy Saloni Dash Amit Sharma V. Balasubramanian CML 29 2 0 22 Oct 2022
Contrastive Learning Inverts the Data Generating Process Roland S. Zimmermann Yash Sharma Steffen Schneider Matthias Bethge Wieland Brendel SSL 236 207 0 17 Feb 2021
Weakly-Supervised Disentanglement Without Compromises Francesco Locatello Ben Poole Gunnar Rätsch Bernhard Schölkopf Olivier Bachem Michael Tschannen CoGe OOD DRL 171 313 0 07 Feb 2020
Masked Gradient-Based Causal Structure Learning Ignavier Ng Shengyu Zhu Zhuangyan Fang Haoyang Li Zhitang Chen Jun Wang CML 75 117 0 18 Oct 2019