Naman Agarwal

Email: namana[@]cs[dot]princeton[dot]edu

I am a PhD student in the OptiML and the Theory group at the Computer Science Department at Princeton University I am fortunate to be advised by Elad Hazan

Previously, I graduated with a Masters of Science in Computer Science at the University of Illinois Urbana-Champaign. I was advised at UIUC by Prof. Alexandra Kolla

I did my undergraduate at the Computer Science and Engineering Department at IIT Bombay in 2012. Here I was advised by Prof. Abhiram Ranade.

CV  /  Google Scholar  /  Github  /  LinkedIn

Research

I am interested in Optimization for Machine Learning with a focus on faster optimization methods for Deep Learning. Recently we have focused on provably fast second order methods for Non-convex (FastCubic) and Convex (LiSSA) optimization. I am also interested in data privacy, distributed optimization and online learning. Following are links to my papers in chronologcal order.

Communication Efficient Differentially Private Mechanisms for Distributed Mean Estimation
Naman Agarwal, Ananda Theertha Suresh, Felix Yu and Sanjiv Kumar
In preparation

Lower Bounds for Higher-Order Convex Optimization
Naman Agarwal, Elad Hazan
Under Submission to STOC, 2018
Arxiv

Leverage Score Sampling for Faster Accelerated Regression and ERM
Naman Agarwal, Sham Kakade, Rahul Kidambi, Praneeth Nethrapalli, Aaron Sidford and Yin Tat-Lee
Under Submission to COLT, 2018
Arxiv

The Price of Differential Privacy For Online Learning
Naman Agarwal and Karan Singh
International Conference on Machine Learning (ICML) - 2017
Arxiv

Finding Approximate Local Minima for Nonconvex Optimization in Linear Time
Naman Agarwal, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan and Tengyu Ma
Symposium on Theory of Computing (STOC), 2017
Arxiv

Second Order Stochastic Optimization in Linear Time
Naman Agarwal, Brian Bullins and Elad Hazan
Journal of Machine Learning Research (JMLR), 2017
Preliminary results presented at the Optimization Methods for the Next Generation of Machine Learning workshop at ICML 2016
Arxiv/ Code/ Poster

Multisection in the Stochastic Block Model using Semidefinite Programming
Naman Agarwal, Afonso S. Bandeira, Konstantinos Koiliaris and Alexandra Kolla
Compressed Sensing and Its Applications: Second International MATHEON Conference - 2015
Arxiv

On the Expansion of Group-based Lifts
Naman Agarwal, Karthekeyan Chandrasekaran, Alexandra Kolla and Vivek Madan
21st International Workshop on Randomization and Computation (RANDOM) - 2017
Arxiv

Unique Games on the Hypercube
Naman Agarwal, Guy Kindler, Alexandra Kolla and Luca Trevisan
Chicago Journal of Theoretical Computer Science - 2014
Link


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