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Ananya Kumar
Ananya Kumar
PhD Student, Stanford University
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
3402021
Verified Uncertainty Calibration
A Kumar, P Liang, T Ma
Neural Information Processing Systems (NeurIPS), 2019
1792019
Understanding Self-Training for Gradual Domain Adaptation
A Kumar, T Ma, P Liang
International Conference on Machine Learning (ICML), 2020
1002020
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
J Uesato*, A Kumar*, C Szepesvari*, T Erez, A Ruderman, K Anderson, ...
International Conference on Learning Representations (ICLR), 2019
572019
Consistent generative query networks
A Kumar, SM Eslami, DJ Rezende, M Garnelo, F Viola, E Lockhart, ...
NeurIPS workshop on Bayesian Deep Learning, 2018
40*2018
Self-training avoids using spurious features under domain shift
Y Chen*, C Wei*, A Kumar, T Ma
Neural Information Processing Systems (NeurIPS), 2020
342020
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution
A Kumar, A Raghunathan, R Jones, T Ma, P Liang
International Conference on Learning Representations (ICLR), 2022
312022
Selective Classification Can Magnify Disparities Across Groups
E Jones*, S Sagawa*, PW Koh*, A Kumar, P Liang
International Conference on Learning Representations (ICLR), 2021
212021
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
202021
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
162022
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
A Ruderman, R Everett, B Sikder, H Soyer, C Beattie, J Uesato, A Kumar, ...
ICLR SafeML Workshop, 2019
10*2019
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
9*2022
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
62018
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
3*2022
Beyond separability: Analyzing the linear transferability of contrastive representations to related subpopulations
JZ HaoChen, C Wei, A Kumar, T Ma
Neural Information Processing Systems (NeurIPS), 2022
32022
Parallel functional arrays
A Kumar, GE Blelloch, R Harper
Principles of Programming Languages (POPL) 52 (1), 706-718, 2017
32017
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets
F Tajwar, A Kumar*, SM Xie*, P Liang
ICML UDL Workshop, 2021
22021
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Articles 1–17