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Found 10 papers, 2 papers with code
A Sinkhorn-type Algorithm for Constrained Optimal Transport
We derive the corresponding entropy regularization formulation and introduce a Sinkhorn-type algorithm for such constrained OT problems supported by theoretical guarantees.
Accelerating Sinkhorn Algorithm with Sparse Newton Iterations
To achieve possibly super-exponential convergence, we present Sinkhorn-Newton-Sparse (SNS), an extension to the Sinkhorn algorithm, by introducing early stopping for the matrix scaling steps and a second stage featuring a Newton-type subroutine.
Multi-Objective Optimization via Wasserstein-Fisher-Rao Gradient Flow
Multi-objective optimization (MOO) aims to optimize multiple, possibly conflicting objectives with widespread applications.
Optimal Algorithms for Stochastic Bilevel Optimization under Relaxed Smoothness Conditions
In this paper, we introduce a novel fully single-loop and Hessian-inversion-free algorithmic framework for stochastic bilevel optimization and present a tighter analysis under standard smoothness assumptions (first-order Lipschitzness of the UL function and second-order Lipschitzness of the LL function).
A One-Sample Decentralized Proximal Algorithm for Non-Convex Stochastic Composite Optimization
We focus on decentralized stochastic non-convex optimization, where $n$ agents work together to optimize a composite objective function which is a sum of a smooth term and a non-smooth convex term.
Field-wise Embedding Size Search via Structural Hard Auxiliary Mask Pruning for Click-Through Rate Prediction
However, most existing NAS-based works suffer from expensive computational costs, the curse of dimensionality of the search space, and the discrepancy between continuous search space and discrete candidate space.
A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization
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 $T$ functions and the constraint set is a closed convex set.
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order Stochastic Gradient Algorithm
Statistical machine learning models trained with stochastic gradient algorithms are increasingly being deployed in critical scientific applications.
Improved Complexities for Stochastic Conditional Gradient Methods under Interpolation-like Conditions
We analyze stochastic conditional gradient methods for constrained optimization problems arising in over-parametrized machine learning.
Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noise
In this paper, we propose a new continuous neural network framework called Neural Stochastic Differential Equation (Neural SDE) network, which naturally incorporates various commonly used regularization mechanisms based on random noise injection.
