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Ashok Cutkosky
Ashok Cutkosky
Boston University
Verified email at cutkosky.com - Homepage
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
16612015
Momentum-based variance reduction in non-convex sgd
A Cutkosky, F Orabona
Advances in neural information processing systems 32, 2019
3372019
Black-box reductions for parameter-free online learning in banach spaces
A Cutkosky, F Orabona
Conference On Learning Theory, 1493-1529, 2018
1192018
Momentum improves normalized sgd
A Cutkosky, H Mehta
International conference on machine learning, 2260-2268, 2020
982020
Long range language modeling via gated state spaces
H Mehta, A Gupta, A Cutkosky, B Neyshabur
arXiv preprint arXiv:2206.13947, 2022
862022
Online learning without prior information
A Cutkosky, K Boahen
arXiv preprint arXiv:1703.02629, 2017
692017
Online learning with imperfect hints
A Bhaskara, A Cutkosky, R Kumar, M Purohit
International Conference on Machine Learning, 822-831, 2020
512020
Anytime online-to-batch, optimism and acceleration
A Cutkosky
International conference on machine learning, 1446-1454, 2019
502019
Parameter-free, dynamic, and strongly-adaptive online learning
A Cutkosky
International Conference on Machine Learning, 2250-2259, 2020
472020
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
412021
Large scale transfer learning for differentially private image classification
H Mehta, A Thakurta, A Kurakin, A Cutkosky
arXiv preprint arXiv:2205.02973, 2022
392022
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
342021
Artificial constraints and hints for unbounded online learning
A Cutkosky
Conference on Learning Theory, 874-894, 2019
342019
Understanding adamw through proximal methods and scale-freeness
Z Zhuang, M Liu, A Cutkosky, F Orabona
Transactions on Machine Learning Research, 2022
292022
Online convex optimization with unconstrained domains and losses
A Cutkosky, KA Boahen
Advances in neural information processing systems 29, 2016
282016
Distributed stochastic optimization via adaptive SGD
A Cutkosky, R Busa-Fekete
Advances in Neural Information Processing Systems 31, 2018
272018
Combining online learning guarantees
A Cutkosky
Conference on Learning Theory, 895-913, 2019
252019
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
212023
Parameter-free mirror descent
A Jacobsen, A Cutkosky
Conference on Learning Theory, 4160-4211, 2022
192022
Kernel truncated randomized ridge regression: Optimal rates and low noise acceleration
KS Jun, A Cutkosky, F Orabona
Advances in neural information processing systems 32, 2019
182019
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