89 citations · 94 across the 8 of their papers we have counts for
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Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
Xuxing Chen, Tesi Xiao, Krishnakumar Balasubramanian
Stochastic Bilevel optimization usually involves minimizing an upper-level (UL) function that is dependent on the arg-min of a strongly-convex lower-level (LL) function. Several al…
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization
Tesi Xiao, Xuxing Chen, Krishnakumar Balasubramanian +1
We focus on decentralized stochastic non-convex optimization, where agents work together to optimize a composite objective function which is a sum of a smooth term and a non-sm…
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
We propose a projection-free conditional gradient-type algorithm for smooth stochastic multi-level composition optimization, where the objective function is a nested composition of…
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions
Tesi Xiao, Krishnakumar Balasubramanian, Saeed Ghadimi
We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning. We show that one could leverage the interpol…