Mikhail Khodak 个人学术档案

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Ameet TalwalkarCMU在 cmu.edu 的电子邮件经过验证
Nikunj SaunshiResearch Scientist, Google在 google.com 的电子邮件经过验证
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
Sanjeev AroraProfessor of Computer Science, Princeton University在 cs.princeton.edu 的电子邮件经过验证
Liam LiCarnegie Mellon University在 liamcli.com 的电子邮件经过验证
Kiran VodrahalliResearch Scientist, Google DeepMind在 google.com 的电子邮件经过验证
Hrishikesh KhandeparkarQuantitative Researcher, Citadel Securities在 alumni.princeton.edu 的电子邮件经过验证
Nicholas RobertsPhD student UW-Madison在 cs.wisc.edu 的电子邮件经过验证
Renbo TuUniversity of Toronto在 mail.utoronto.ca 的电子邮件经过验证
Virginia SmithCarnegie Mellon University在 cmu.edu 的电子邮件经过验证
Junhong ShenPh.D. student in Machine Learning, Carnegie Mellon University在 andrew.cmu.edu 的电子邮件经过验证
Tian LiCarnegie Mellon University在 cmu.edu 的电子邮件经过验证
Jeffrey LiUniversity of Washington在 cs.washington.edu 的电子邮件经过验证
Brandon M. StewartPrinceton University在 princeton.edu 的电子邮件经过验证
Tengyu MAStanford University在 stanford.edu 的电子邮件经过验证
Andrej RisteskiCarnegie Mellon University在 andrew.cmu.edu 的电子邮件经过验证
Sergei VassilvitskiiGoogle在 google.com 的电子邮件经过验证
Nicolò FusiPartner Researcher, Microsoft Research在 microsoft.com 的电子邮件经过验证
Neil TenenholtzMicrosoft Research在 microsoft.com 的电子邮件经过验证
Tri DaoPrinceton University, Together AI在 princeton.edu 的电子邮件经过验证

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Mikhail Khodak

CMU

在 cs.princeton.edu 的电子邮件经过验证


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Advances and open problems in federated learning P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ... Foundations and trends® in machine learning 14 (1–2), 1-210, 2021	5802	2021
A theoretical analysis of contrastive unsupervised representation learning S Arora, H Khandeparkar, M Khodak, O Plevrakis, N Saunshi ICML, 2019	773*	2019
Adaptive gradient-based meta-learning methods M Khodak, MF Balcan, A Talwalkar NeurIPS, 2019	370	2019
A large self-annotated corpus for sarcasm M Khodak, N Saunshi, K Vodrahalli LREC, 2017	268	2017
Provable guarantees for gradient-based meta-learning M Khodak, MF Balcan, A Talwalkar ICML, 2019	166*	2019
Differentially private meta-learning J Li, M Khodak, S Caldas, A Talwalkar ICLR, 2019	142	2019
A la carte embedding: Cheap but effective induction of semantic feature vectors M Khodak, N Saunshi, Y Liang, T Ma, B Stewart, S Arora ACL, 2018	119	2018
Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing M Khodak, R Tu, T Li, L Li, MFF Balcan, V Smith, A Talwalkar Advances in Neural Information Processing Systems 34, 19184-19197, 2021	80*	2021
Geometry-Aware Gradient Algorithms for Neural Architecture Search L Li, M Khodak, MF Balcan, A Talwalkar ICLR, 2020	76	2020
A compressed sensing view of unsupervised text embeddings, bag-of-n-grams, and LSTMs S Arora, M Khodak, N Saunshi, K Vodrahalli ICLR, 2018	49	2018
Initialization and Regularization of Factorized Neural Layers M Khodak, N Tenenholtz, L Mackey, N Fusi ICLR, 2021	48	2021
Automated wordnet construction using word embeddings M Khodak, A Risteski, C Fellbaum, S Arora SENSE, 12-23, 2017	44	2017
NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks R Tu, N Roberts, M Khodak, J Shen, F Sala, A Talwalkar	41*	2022
Learning cloud dynamics to optimize spot instance bidding strategies M Khodak, L Zheng, AS Lan, C Joe-Wong, M Chiang INFOCOM, 2762-2770, 2018	30	2018
A deeper look at zero-cost proxies for lightweight NAS C White, M Khodak, R Tu, S Shah, S Bubeck ICLR Blog Track 445, 2022	28	2022
Direct fusion drive for interstellar exploration SA Cohen, C Swanson, N McGreivy, A Raja, E Evans, P Jandovitz, ... JBIS-Journal of the British Interplanetary Society 72 (2), 37-50, 2019	27	2019
Rethinking neural operations for diverse tasks N Roberts, M Khodak, T Dao, L Li, C Ré, A Talwalkar Advances in Neural Information Processing Systems 34, 15855-15869, 2021	26	2021
Learning predictions for algorithms with predictions M Khodak, MFF Balcan, A Talwalkar, S Vassilvitskii Advances in Neural Information Processing Systems 35, 3542-3555, 2022	23	2022
A sample complexity separation between non-convex and convex meta-learning N Saunshi, Y Zhang, M Khodak, S Arora ICML, 2020	21	2020
Cross-modal fine-tuning: Align then refine J Shen, L Li, LM Dery, C Staten, M Khodak, G Neubig, A Talwalkar International Conference on Machine Learning, 31030-31056, 2023	19*	2023

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