229 citations · 547 across the 25 of their papers we have counts for
9 papers · 1 filter
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
We consider the classical setting of optimizing a nonsmooth Lipschitz continuous convex function over a convex constraint set, when having access to a (stochastic) first-order orac…
No quantum speedup over gradient descent for non-smooth convex optimization
Ankit Garg, Robin Kothari, Praneeth Netrapalli +1
We study the first-order convex optimization problem, where we have black-box access to a (not necessarily smooth) function and its (sub)gradient. O…
Learning Minimax Estimators via Online Learning
Kartik Gupta, Arun Sai Suggala, Adarsh Prasad +2
We consider the problem of designing minimax estimators for estimating the parameters of a probability distribution. Unlike classical approaches such as the MLE and minimum distanc…
Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms
Guy Bresler, Prateek Jain, Dheeraj Nagaraj +2
We study the problem of least squares linear regression where the data-points are dependent and are sampled from a Markov chain. We establish sharp information theoretic minimax lo…
Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games
Arun Sai Suggala, Praneeth Netrapalli
We consider the problem of online learning and its application to solving minimax games. For the online learning problem, Follow the Perturbed Leader (FTPL) is a widely studied alg…
The Pitfalls of Simplicity Bias in Neural Networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan +2
Several works have proposed Simplicity Bias (SB)---the tendency of standard training procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why neu…