In brief, my research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently, and discover new knowledge alongside us. The topics I work on span causal representation learning to robust supervised prediction. I'm interested in both technical results and practical algorithms that work for data such as text, images, networks, or multiple modalities.
Dhanya Sridhar, Hal Daumé, D. Blei
Transactions of the Association for Computational Linguistics, 2022
Elliot I. Layne, Dhanya Sridhar, Jason S. Hartford, M. Blanchette
Gemma E. Moran, Dhanya Sridhar, Yixin Wang, D. Blei
Transactions of Machine Learning Research, To Appear, 2022
Amir Feder, Katherine A. Keith, Emaad A. Manzoor, Reid Pryzant, Dhanya Sridhar, Zach Wood-Doughty, Jacob Eisenstein, Justin Grimmer, Roi Reichart, Margaret E. Roberts, Brandon M Stewart, Victor Veitch, Diyi Yang
IFT 6168, Winter 2022