Ananya Kumar

Citirano

	Sve	Od 2020.
Citati	8972	8928
H-indeks	21	21
i10-indeks	24	23

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201920202021202220232024202525 93 259 946 2658 4195 757

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Na temelju uvjeta financiranja

Suautori

Percy LiangAssociate Professor of Computer Science, Stanford UniversityPotvrđena adresa e-pošte na cs.stanford.edu
Tengyu MAStanford UniversityPotvrđena adresa e-pošte na stanford.edu
Aditi RaghunathanAssistant professor, Carnegie Mellon UniversityPotvrđena adresa e-pošte na cmu.edu
Sang Michael XieMeta GenAIPotvrđena adresa e-pošte na cs.stanford.edu
Chelsea FinnStanford University, Physical IntelligencePotvrđena adresa e-pošte na cs.stanford.edu
Fahim TajwarPhD Student, Machine Learning, Carnegie Mellon UniversityPotvrđena adresa e-pošte na andrew.cmu.edu
Kendrick ShenPotvrđena adresa e-pošte na alumni.stanford.edu
Pang Wei KohUniversity of WashingtonPotvrđena adresa e-pošte na cs.washington.edu
Shiori SagawaPhD Student, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
Haochen ZhangPhD Student, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
Colin WeiStanford UniversityPotvrđena adresa e-pošte na stanford.edu
Pushmeet KohliDeepMindPotvrđena adresa e-pošte na google.com
Zico KolterCarnegie Mellon UniversityPotvrđena adresa e-pošte na cs.cmu.edu
sachin goyalCMU, Microsoft ResearchPotvrđena adresa e-pošte na microsoft.com
S. M. Ali EslamiGoogle DeepMindPotvrđena adresa e-pošte na google.com
Edward LockhartDeepMindPotvrđena adresa e-pošte na deepmind.com
Murray ShanahanImperial College London / DeepMindPotvrđena adresa e-pošte na imperial.ac.uk
Rishi BommasaniCS PhD, Stanford UniversityPotvrđena adresa e-pošte na stanford.edu
Csaba SzepesvariDeepMind & University of AlbertaPotvrđena adresa e-pošte na cs.ualberta.ca
Yining ChenOpenAIPotvrđena adresa e-pošte na openai.com

Prati

Ananya Kumar

Research Scientist, OpenAI

Potvrđena adresa e-pošte na cs.stanford.edu - Početna stranica

Machine Learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
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	4802	2021
Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... Transactions on Machine Learning Research (TMLR), 2023	1217	2023
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	735	2022
Verified Uncertainty Calibration A Kumar, P Liang, T Ma Neural Information Processing Systems (NeurIPS), 2019	418	2019
Understanding Self-Training for Gradual Domain Adaptation A Kumar, T Ma, P Liang International Conference on Machine Learning (ICML), 2020	263	2020
Gpt-4o system card A Hurst, A Lerer, AP Goucher, A Perelman, A Ramesh, A Clark, AJ Ostrow, ... arXiv preprint arXiv:2410.21276, 2024	219	2024
Surgical fine-tuning improves adaptation to distribution shifts Y Lee, AS Chen, F Tajwar, A Kumar, H Yao, P Liang, C Finn International Conference on Learning Representations (ICLR), 2023	208	2023
Finetune like you pretrain: Improved finetuning of zero-shot vision models S Goyal, A Kumar, S Garg, Z Kolter, A Raghunathan Conference on Computer Vision and Pattern Recognition (CVPR), 2023	147	2023
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	136	2022
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	116	2022
Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? R Bommasani, K Creel, A Kumar, D Jurafsky, P Liang Advances in Neural Information Processing Systems (NeurIPS), 2022	91	2022
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	89	2019
Self-training avoids using spurious features under domain shift Y Chen, C Wei, A Kumar, T Ma Neural Information Processing Systems (NeurIPS), 2020	85	2020
Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024	73	2024
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	65	2021
Selective Classification Can Magnify Disparities Across Groups E Jones, S Sagawa, PW Koh*, A Kumar, P Liang International Conference on Learning Representations (ICLR), 2021	60	2021
Consistent generative query networks A Kumar, SM Eslami, DJ Rezende, M Garnelo, F Viola, E Lockhart, ... NeurIPS workshop on Bayesian Deep Learning, 2018	50*	2018
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	42	2022
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	41*	2022
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	23	2021

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