| A comparative study of transformer-based language models on extractive question answering K Pearce, T Zhan, A Komanduri, J Zhan arXiv preprint arXiv:2110.03142, 2021 | 45* | 2021 |
| Scm-vae: Learning identifiable causal representations via structural knowledge A Komanduri, Y Wu, W Huang, F Chen, X Wu IEEE International Conference on Big Data (Big Data), 1014-1023, 2022 | 11 | 2022 |
| Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms A Komanduri, Y Wu, F Chen, X Wu International Joint Conference on Artificial Intelligence (IJCAI), 4308-4316, 2024 | 10 | 2024 |
| From identifiable causal representations to controllable counterfactual generation: A survey on causal generative modeling A Komanduri, X Wu, Y Wu, F Chen Transactions on Machine Learning Research (TMLR), 2024 | 10 | 2024 |
| Neighborhood random walk graph sampling for regularized Bayesian graph convolutional neural networks A Komanduri, J Zhan IEEE International Conference on Machine Learning and Applications (ICMLA …, 2021 | 4 | 2021 |
| Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Models A Komanduri, C Zhao, F Chen, X Wu European Conference on Artificial Intelligence (ECAI), 2516-2523, 2024 | 2 | 2024 |
| Toward Causal Generative Modeling: From Representation to Generation A Komanduri Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 39, 2025 | | 2025 |
Xintao WuUniversity of ArkansasBestätigte E-Mail-Adresse bei uark.edu
