Localizing Unsynchronized Sensors With Unknown Sources

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We consider the problem of finding a saddle point for the convex-concave objective

\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)

, where

f

is a convex function with locally Lipschitz gradient and

g

is convex and possibly non-smooth. We propose an ...

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Localizing Unsynchronized Sensors With Unknown Sources

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Perturbed Utility Stochastic Traffic Assignment

The complexity of quantum support vector machines

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Universal and adaptive methods for robust stochastic optimization

Safe Zeroth-Order Convex Optimization Using Quadratic Local Approximations

Stochastic homogenization of degenerate integral functionals with linear growth

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

On The Convergence Of Stochastic Primal-Dual Hybrid Gradient

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Stochastic distributed learning with gradient quantization and double-variance reduction

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

A combined Control by Interconnection—Model Predictive Control design for constrained Port-Hamiltonian systems

Proximal Point Imitation Learning

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima’s Replica Formula)

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

A first-order primal-dual method with adaptivity to local smoothness

Adaptation in Stochastic Algorithms: From Nonsmooth Optimization to Min-Max Problems and Beyond