Papers by Krishnateja Killamsetty
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming (2022.findings-acl)
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Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa
| Challenge: | supervised machine learning requires large amounts of labeled data to train models. |
| Approach: | They propose a framework to generate human-interpretable labeling functions . they propose to learn a model on the same labeled dataset and unlabeled data . |
| Outcome: | The proposed framework outperforms prior approaches on several text classification datasets. |
Semi-Supervised Data Programming with Subset Selection (2021.findings-acl)
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| Challenge: | Several approaches to improve annotation cost have been proposed to use large amounts of labelled training data. |
| Approach: | They propose a semi-supervised data programming paradigm that uses weak supervision and semi-supervised loss functions to augment small amounts of labelled data with a large unlabelled dataset. |
| Outcome: | The proposed framework outperforms the current state-of-the-art on seven publicly available datasets. |
INGENIOUS: Using Informative Data Subsets for Efficient Pre-Training of Language Models (2023.findings-emnlp)
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H S V N S Kowndinya Renduchintala, Krishnateja Killamsetty, Sumit Bhatia, Milan Aggarwal, Ganesh Ramakrishnan, Rishabh Iyer, Balaji Krishnamurthy
| Challenge: | Pre-trained language models have a remarkable improvement in generalization capability . however, this leads to prohibitively long training times and a detrimental environmental impact . |
| Approach: | They propose to use submodular optimization to select highly informative subsets of training data to train multiple PTLMs using only fractions of data. |
| Outcome: | The proposed framework achieves 99% of the performance of fully-trained models using only fraction of training data. |