Kahini Wadhawan — accepted AI-conference papers

2023

CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generation

Rahul Madhavan, Rishabh Garg, Kahini Wadhawan, Sameep Mehta

ACL 2023findings

We propose a method to control the attributes of Language Models (LMs) for the text generation task using Causal Average Treatment Effect (ATE) scores and counterfactual augmentation. We explore this method, in the context of LM detoxification, and propose the Causally Fair Language (CFL) architectu…

Cited by 5 Source ↗PDF ↗Scholar ↗

2018

Co-regularized Alignment for Unsupervised Domain Adaptation

Abhishek Kumar, Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman +1

NeurIPS 2018poster

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a target domain whose distribution differs from the training data distribution, referred as the source domain. It can be expensive or even infeasible to obtain required amount…

Cited by 215 Source ↗PDF ↗code ↗Scholar ↗