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Akshay Agrawal
Akshay Agrawal
Verified email at cs.stanford.edu - Homepage
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
8012018
Differentiable convex optimization layers
A Agrawal, B Amos, S Barratt, S Boyd, S Diamond, JZ Kolter
Advances in Neural Information Processing Systems (NeurIPS), 2019
6222019
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
1192019
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
1182015
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
932019
Minimum-distortion embedding
A Agrawal, A Ali, S Boyd
Foundations and Trends in Machine Learning 14 (3), 221-378, 2021
592021
Learning convex optimization control policies
A Agrawal, S Barratt, S Boyd, B Stellato
Learning for Dynamics and Control, 2019
592019
Disciplined geometric programming
A Agrawal, S Diamond, S Boyd
Optimization Letters 13 (5), 961–976, 2019
532019
Disciplined quasiconvex programming
A Agrawal, S Boyd
Optimization Letters, 2020
482020
Learning convex optimization models
A Agrawal, S Barratt, S Boyd
arXiv preprint arXiv:2006.04248, 2020
382020
The Stanford MOOCPosts Dataset
A Agrawal, J Venkatraman, A Paepcke
212014
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
72022
Differentiating through log-log convex programs
A Agrawal, S Boyd
arXiv preprint arXiv:2004.12553, 2020
42020
Cosine siamese models for stance detection
A Agrawal, D Chin, K Chen
Technical Report, 2017
42017
Xavier : A reinforcement-learning approach to TCP congestion control
A Agrawal
Technical Report, 2016
32016
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Articles 1–15