Publications

Surveys

[TMLR'24] From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on
Causal Generative Modeling
Aneesh Komanduri, Xintao Wu, Yongkai Wu, Feng Chen.
Transactions on Machine Learning Research, 2024.
[arXiv] [OpenReview]

[ECAI'24] Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models
Aneesh Komanduri, Chen Zhao, Feng Chen, Xintao Wu.
Proceedings of the 27th European Conference on Artificial Intelligence, 2024. (Acceptance rate: 23.1%)
*Also appeared in non-archival CVPR 2024 Workshop on Generative Models for Computer Vision
[arXiv] [Proceedings] [Poster] [Code]
[IJCAI'24] Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms
Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu.
Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2024. (Acceptance rate: 13.9%)
Long Oral: top 2.3%
*Also appeared in non-archival NeurIPS 2023 Workshop on Causal Representation Learning
[arXiv] [Proceedings] [Poster] [Code]

[BigData'22] SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
Aneesh Komanduri, Yongkai Wu, Feng Chen, Xintao Wu.
Proceedings of the IEEE International Conference on Big Data, 2022.
[Proceedings] [Code]

[ICMLA'21] Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional
Neural Networks
Aneesh Komanduri, Justin Zhan.
Proceedings of the IEEE International Conference on Machine Learning and Applications, 2021.
[Proceedings] [Code]
[arXiv] A Comparative Study of Transformer-based Language Models on Extractive Question Answering
Kate Pearce, Tiffany Zhan, Aneesh Komanduri, Justin Zhan.
arXiv Preprint, 2021.
[arXiv]