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

Aneesh Komanduri
Xintao Wu
Yongkai Wu
Feng Chen
ArXiv (abs)PDFHTML

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

5 / 5 papers shown
Title
Identifiability of Deep Polynomial Neural Networks
Identifiability of Deep Polynomial Neural Networks
K. Usevich
Clara Dérand
Ricardo Augusto Borsoi
Marianne Clausel
10
0
0
20 Jun 2025
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models
Tian Xia
Fabio De Sousa Ribeiro
Rajat Rasal
Avinash Kori
Raghav Mehta
Ben Glocker
DiffM
31
0
0
17 Jun 2025
Diffusion Counterfactual Generation with Semantic Abduction
Diffusion Counterfactual Generation with Semantic Abduction
Rajat Rasal
Avinash Kori
Fabio De Sousa Ribeiro
Tian Xia
Ben Glocker
DiffM
14
0
0
09 Jun 2025
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
CausalVLBench: Benchmarking Visual Causal Reasoning in Large Vision-Language Models
Aneesh Komanduri
Karuna Bhaila
Xintao Wu
ReLMLRMELM
15
0
0
21 May 2025
On the Challenges and Opportunities in Generative AI
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
284
22
0
28 Feb 2024

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