29 citations · 73 across the 7 of their papers we have counts for
10 papers
Lifted Primal-Dual Method for Bilinearly Coupled Smooth Minimax Optimization
Kiran Koshy Thekumparampil, Niao He, Sewoong Oh
We study the bilinearly coupled minimax problem: , where and are both strongly convex smooth functions and admit first-order gra…
Sample Efficient Linear Meta-Learning by Alternating Minimization
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
Meta-learning synthesizes and leverages the knowledge from a given set of tasks to rapidly learn new tasks using very little data. Meta-learning of linear regression tasks, where t…
Efficient Algorithms for Federated Saddle Point Optimization
Charlie Hou, Kiran K. Thekumparampil, Giulia Fanti +1
We consider strongly convex-concave minimax problems in the federated setting, where the communication constraint is the main bottleneck. When clients are arbitrarily heterogeneous…
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…
Efficient Algorithms for Smooth Minimax Optimization
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
This paper studies first order methods for solving smooth minimax optimization problems where is smooth and is concave for each…
Robust conditional GANs under missing or uncertain labels
Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan
Matching the performance of conditional Generative Adversarial Networks with little supervision is an important task, especially in venturing into new domains. We design a new trai…