Akshay Agrawal

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Stephen BoydProfessor of Electrical Engineering, Computer Science, and Management Science, StanfordVerified email at stanford.edu
Steven DiamondGridmaticVerified email at cs.stanford.edu
Shane BarrattPhD Student, Stanford UniversityVerified email at stanford.edu
Brandon AmosMeta AI (FAIR)Verified email at fb.com
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
Jagadish VenkatramanTech Lead, LinkedInVerified email at cs.stanford.edu
Walaa MoursiUniversity of WaterlooVerified email at uwaterloo.ca
Enzo BussetiIndependent ScholarVerified email at stanford.edu
Alexandre PassosGoogle researchVerified email at cs.umass.edu
Rajat MongaGoogleVerified email at google.com
Shanqing CaiGoogleVerified email at google.com
Allen LavoieVerified email at google.com
Bartolomeo StellatoAssistant Professor, Princeton UniversityVerified email at princeton.edu
Alnur AliStanford UniversityVerified email at stanford.edu
Delenn ChinVerified email at cs.stanford.edu
Fiodar KazhamiakaMicrosoftVerified email at microsoft.com
Matei ZahariaStanford DAWN Lab and DatabricksVerified email at cs.stanford.edu
Deepak NarayananMicrosoft ResearchVerified email at microsoft.com

Akshay Agrawal

PhD, Stanford University

Verified email at cs.stanford.edu - Homepage

Optimization Machine Learning Computer Science


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	567	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	388	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	100	2015
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	77	2019
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	74	2019
Learning convex optimization control policies A Agrawal, S Barratt, S Boyd, B Stellato Learning for Dynamics and Control, 2019	44	2019
Disciplined geometric programming A Agrawal, S Diamond, S Boyd Optimization Letters 13 (5), 961–976, 2019	40	2019
Disciplined quasiconvex programming A Agrawal, S Boyd Optimization Letters, 2020	34	2020
Minimum-distortion embedding A Agrawal, A Ali, S Boyd Foundations and Trends in Machine Learning 14 (3), 221-378, 2021	29	2021
Learning convex optimization models A Agrawal, S Barratt, S Boyd arXiv preprint arXiv:2006.04248, 2020	20	2020
The Stanford MOOCPosts Dataset A Agrawal, J Venkatraman, A Paepcke	15	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	4	2022
Cosine siamese models for stance detection A Agrawal, D Chin, K Chen Technical Report, 2017	4	2017
Differentiating through log-log convex programs A Agrawal, S Boyd arXiv preprint arXiv:2004.12553, 2020	3	2020
Xavier : A reinforcement-learning approach to TCP congestion control A Agrawal Technical Report, 2016	3	2016

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