Akshay Agrawal 个人学术档案

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引用	2115	2048
h 指数	11	11
i10 指数	11	11

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Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, Stanford在 stanford.edu 的电子邮件经过验证
Steven DiamondGridmatic在 cs.stanford.edu 的电子邮件经过验证
Shane BarrattPhD Student, Stanford University在 stanford.edu 的电子邮件经过验证
Brandon AmosMeta在 fb.com 的电子邮件经过验证
Zico KolterCarnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
Walaa M. MoursiUniversity of Waterloo在 uwaterloo.ca 的电子邮件经过验证
Enzo Busseti在 stanford.edu 的电子邮件经过验证
Jagadish VenkatramanTech Lead, LinkedIn在 cs.stanford.edu 的电子邮件经过验证
Alexandre PassosOpenAI在 cs.umass.edu 的电子邮件经过验证
Rajat MongaGoogle在 google.com 的电子邮件经过验证
Shanqing CaiGoogle在 google.com 的电子邮件经过验证
Allen Lavoie在 google.com 的电子邮件经过验证
Alnur AliStanford University在 stanford.edu 的电子邮件经过验证
Bartolomeo StellatoPrinceton University在 princeton.edu 的电子邮件经过验证
Fiodar KazhamiakaMicrosoft在 microsoft.com 的电子邮件经过验证
Matei ZahariaUC Berkeley and Databricks在 berkeley.edu 的电子邮件经过验证
Deepak NarayananNVIDIA在 nvidia.com 的电子邮件经过验证
Delenn Chin在 cs.stanford.edu 的电子邮件经过验证

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Akshay Agrawal

PhD, Stanford University

在 cs.stanford.edu 的电子邮件经过验证 - 首页

Optimization Machine Learning Computer Science


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
A rewriting system for convex optimization problems A Agrawal, R Verschueren, S Diamond, S Boyd Journal of Control and Decision 5 (1), 42-60, 2018	821	2018
Differentiable convex optimization layers A Agrawal, B Amos, S Barratt, S Boyd, S Diamond, JZ Kolter Advances in Neural Information Processing Systems (NeurIPS), 2019	642	2019
Differentiating through a cone program A Agrawal, S Barratt, S Boyd, E Busseti, WM Moursi Journal of Applied and Numerical Optimization 1 (2), 107–115, 2019	126	2019
YouEDU: Addressing confusion in MOOC discussion forums by recommending instructional video clips A Agrawal, J Venkatraman, S Leonard, A Paepcke International Conference on Educational Data Mining, 297-304, 2015	119	2015
TensorFlow Eager: A multi-stage, Python-embedded DSL for machine learning A Agrawal, AN Modi, A Passos, A Lavoie, A Agarwal, A Shankar, ... Systems for Machine Learning (SysML), 2019	93	2019
Minimum-distortion embedding A Agrawal, A Ali, S Boyd Foundations and Trends in Machine Learning 14 (3), 221-378, 2021	67	2021
Learning convex optimization control policies A Agrawal, S Barratt, S Boyd, B Stellato Learning for Dynamics and Control, 2019	62	2019
Disciplined geometric programming A Agrawal, S Diamond, S Boyd Optimization Letters 13 (5), 961–976, 2019	56	2019
Disciplined quasiconvex programming A Agrawal, S Boyd Optimization Letters, 2020	50	2020
Learning convex optimization models A Agrawal, S Barratt, S Boyd arXiv preprint arXiv:2006.04248, 2020	39	2020
The Stanford MOOCPosts Dataset A Agrawal, J Venkatraman, A Paepcke	21	2014
Allocation of fungible resources via a fast, scalable price discovery method A Agrawal, S Boyd, D Narayanan, F Kazhamiaka, M Zaharia Mathematical Programming Computation, 2022	7	2022
Differentiating through log-log convex programs A Agrawal, S Boyd arXiv preprint arXiv:2004.12553, 2020	5	2020
Cosine siamese models for stance detection A Agrawal, D Chin, K Chen Technical Report, 2017	4	2017
Xavier : A reinforcement-learning approach to TCP congestion control A Agrawal Technical Report, 2016	3	2016

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