Articles with public access mandates - Ananya KumarLearn more
Available somewhere: 7
Extending the wilds benchmark for unsupervised adaptation
S Sagawa*, PW Koh*, T Lee*, I Gao*, SM Xie, K Shen, A Kumar, W Hu, ...
International Conference on Learning Representations (ICLR), 2022
Mandates: US National Science Foundation, US Department of Defense, Marcus and Amalia …
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation
K Shen*, R Jones*, A Kumar*, SM Xie*, JZ HaoChen, T Ma, P Liang
International Conference on Machine Learning (ICML), 2022
Mandates: US National Science Foundation, US Department of Defense
Self-training avoids using spurious features under domain shift
Y Chen*, C Wei*, A Kumar, T Ma
Neural Information Processing Systems (NeurIPS), 2020
Mandates: US National Science Foundation
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness
SM Xie*, A Kumar*, R Jones*, F Khani, T Ma, P Liang
International Conference on Learning Representations (ICLR), 2021
Mandates: US National Science Foundation, US Department of Defense
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
A Kumar, T Ma, P Liang, A Raghunathan
Conference on Uncertainty in Artificial Intelligence (UAI), 2022
Mandates: US National Science Foundation
Approximate Convex Hull of Data Streams
A Blum, V Braverman, A Kumar, H Lang, LF Yang
International Colloquium on Automata, Languages and Programming (ICALP), 2018
Mandates: US National Science Foundation
Parallel functional arrays
A Kumar, GE Blelloch, R Harper
Principles of Programming Languages (POPL) 52 (1), 706-718, 2017
Mandates: US National Science Foundation, US Department of Defense
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