Divyansh Kaushik, PhD Student
https://www.cs.cmu.edu/~dkaushik/
Divyansh Kaushik is the Associate Director for Emerging Technologies and National Security at the Federation of American Scientists. He holds a PhD from Carnegie Mellon University in Pittsburgh, where he focused on designing reliable artificial intelligence systems that align with human values. In addition to his work on Capitol Hill advocating for defense, science, innovation, and immigration issues, he has also played a key role in the establishment of the Congressional Graduate Research and Development Caucus. He is also a frequent contributor to leading publications, including National Defense Magazine, The Dispatch, Daily Caller, and Forbes, sharing his insights and expertise on a variety of topics relevant to national security and innovation.
Research Interests: Human-in-the-Loop Learning, NLP
Publications
Resolving the Human Subjects Status of Machine Learning's Crowdworkers
Divyansh Kaushik, Zachary Lipton, Alex John London
arXiv Preprint, 2022Practical Benefits of Feature Feedback Under Distribution Shift
Anurag Katakkar*, Clay Yoo*, Weiqin Wang*, Zachary Lipton, Divyansh Kaushik
BlackBox NLP workshop, 2022On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
Divyansh Kaushik, Douwe Kiela, Zachary Lipton, Wen-tau Yih
Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), 2021Dynabench: Rethinking Benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, Adina Williams
North American Chapter of the Association for Computational Linguistics (NAACL), 2021Explaining The Efficacy of Counterfactually-Augmented Data
Divyansh Kaushik, Zachary Lipton
International Conference on Learning Representations (ICLR), 2021Learning the Difference that Makes a Difference with Counterfactually-Augmented Data
Divyansh Kaushik, Zachary Lipton
International Conference on Learning Representations (ICLR), 2020Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment
Yifan Wu, Divyansh Kaushik, Ezra Winston, Zachary Lipton
International Conference on Machine Learning (ICML), 2019How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks
Divyansh Kaushik, Zachary Lipton
Empirical Methods in Natural Language Processing (EMNLP), 2018