Ashok Cutkosky

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Francesco OrabonaAssociate Professor, KAUSTVerified email at orabona.com
Harsh MehtaResearch Engineer, Google DeepMindVerified email at google.com
Erez Lieberman AidenBaylor College of MedicineVerified email at erez.com
Ivan BochkovGraduate Student, Baylor College of MedicineVerified email at bcm.edu
Andrew I. JewettCaltechVerified email at caltech.edu
Adrian L. SanbornStanford UniversityVerified email at stanford.edu
Miriam H HuntleyDay Zero DiagnosticsVerified email at fas.harvard.edu
Suhas RaoUniversity of California, San FranciscoVerified email at ucsf.edu
Ioannis Ch. PaschalidisDistinguished Professor of Engineering, Director of the Hariri Institute, Boston UniversityVerified email at bu.edu
Kwabena BoahenProfessor of Bioengineering & Electrical Engineering, Stanford UniversityVerified email at stanford.edu
Zhiyu ZhangPostdoc, Harvard UniversityVerified email at seas.harvard.edu
zhenxun zhuangMetaVerified email at fb.com
Qinzi ZhangPhD Student, Boston UniversityVerified email at bu.edu
Kwang-Sung JunThe University of ArizonaVerified email at cs.arizona.edu
Jiujia ZhangPhD student, Boston UniversityVerified email at bu.edu
Tamás SarlósGoogleVerified email at google.com
Aditya GangradeCarnegie Mellon UniversityVerified email at andrew.cmu.edu
Venkatesh SaligramaAI Professor and Amazon ScholarVerified email at bu.edu
Hoang TranBoston UniversityVerified email at bu.edu
Leon FurchtgottHarvard UniversityVerified email at fas.harvard.edu

Ashok Cutkosky

Boston University

Verified email at cutkosky.com - Homepage

Machine Learning Optimization Online Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Chromatin extrusion explains key features of loop and domain formation in wild-type and engineered genomes AL Sanborn, SSP Rao, SC Huang, NC Durand, MH Huntley, AI Jewett, ... Proceedings of the National Academy of Sciences 112 (47), E6456-E6465, 2015	1766	2015
Momentum-based variance reduction in non-convex sgd A Cutkosky, F Orabona Advances in neural information processing systems 32, 2019	408	2019
Long range language modeling via gated state spaces H Mehta, A Gupta, A Cutkosky, B Neyshabur arXiv preprint arXiv:2206.13947, 2022	196	2022
Black-box reductions for parameter-free online learning in banach spaces A Cutkosky, F Orabona Conference On Learning Theory, 1493-1529, 2018	145	2018
Momentum improves normalized sgd A Cutkosky, H Mehta International conference on machine learning, 2260-2268, 2020	125	2020
Online learning without prior information A Cutkosky, K Boahen arXiv preprint arXiv:1703.02629, 2017	76	2017
Understanding adamw through proximal methods and scale-freeness Z Zhuang, M Liu, A Cutkosky, F Orabona Transactions on machine learning research, 2022	66	2022
Anytime online-to-batch, optimism and acceleration A Cutkosky International conference on machine learning, 1446-1454, 2019	66	2019
Online learning with imperfect hints A Bhaskara, A Cutkosky, R Kumar, M Purohit International Conference on Machine Learning, 822-831, 2020	64	2020
High-probability bounds for non-convex stochastic optimization with heavy tails A Cutkosky, H Mehta Advances in Neural Information Processing Systems 34, 4883-4895, 2021	58	2021
Parameter-free, dynamic, and strongly-adaptive online learning A Cutkosky International Conference on Machine Learning, 2250-2259, 2020	54	2020
Large scale transfer learning for differentially private image classification H Mehta, A Thakurta, A Kurakin, A Cutkosky arXiv preprint arXiv:2205.02973, 2022	46	2022
Dynamic balancing for model selection in bandits and rl A Cutkosky, C Dann, A Das, C Gentile, A Pacchiano, M Purohit International Conference on Machine Learning, 2276-2285, 2021	40	2021
Artificial constraints and hints for unbounded online learning A Cutkosky Conference on Learning Theory, 874-894, 2019	39	2019
Optimal stochastic non-smooth non-convex optimization through online-to-non-convex conversion A Cutkosky, H Mehta, F Orabona International Conference on Machine Learning, 6643-6670, 2023	38	2023
Parameter-free mirror descent A Jacobsen, A Cutkosky Conference on Learning Theory, 4160-4211, 2022	33	2022
Online convex optimization with unconstrained domains and losses A Cutkosky, KA Boahen Advances in neural information processing systems 29, 2016	31	2016
Distributed stochastic optimization via adaptive SGD A Cutkosky, R Busa-Fekete Advances in Neural Information Processing Systems 31, 2018	27	2018
Combining online learning guarantees A Cutkosky Conference on Learning Theory, 895-913, 2019	26	2019
PDE-based optimal strategy for unconstrained online learning Z Zhang, A Cutkosky, I Paschalidis International Conference on Machine Learning, 26085-26115, 2022	21	2022

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