On causally disentangled representations AG Reddy, VN Balasubramanian AAAI 2022, 2022 | 23 | 2022 |
Matching learned causal effects of neural networks with domain priors SS Kancheti, AG Reddy, VN Balasubramanian, A Sharma ICML 2022, 2022 | 11 | 2022 |
Causal inference using llm-guided discovery A Vashishtha, AG Reddy, A Kumar, S Bachu, VN Balasubramanian, ... AAAI 2024 workshop on “Are LLMs simply causal parrots?”, 2023 | 7 | 2023 |
CANDLE: An Image Dataset for Causal Analysis in Disentangled Representations AG Reddy, VN Balasubramanian Best paper @ Workshop on Causality in Vision - CVPR, 2021 | 4 | 2021 |
Rethinking Counterfactual Data Augmentation Under Confounding AG Reddy, S Bachu, S Dash, C Sharma, A Sharma, VN Balasubramanian arXiv preprint arXiv:2305.18183, 2023 | 2 | 2023 |
On Counterfactual Data Augmentation Under Confounding AG Reddy, S Bachu, S Dash, C Sharma, A Sharma, VN Balasubramanian NeurIPS 2022 workshop on CML4Impact, 2023 | 2* | 2023 |
Causality in Neural Networks-An Extended Abstract A Gowtham Reddy AIES 2021, 2021 | 2 | 2021 |
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation AG Reddy, VN Balasubramanian AAAI 2024, 2024 | 1* | 2024 |
Towards Learning and Explaining Indirect Causal Effects in Neural Networks AG Reddy, S Bachu, H Pathak, BL Godfrey, VN Balasubramanian, SN Kar AAAI 2024, 2024 | | 2024 |
Debiasing Machine Unlearning with Counterfactual Examples Z Chen, J Wang, J Zhuang, AG Reddy, F Silvestri, J Huang, K Nag, ... | | |