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Safe non-smooth black-box optimization with application to policy search

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For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points. We propose an approach based on log barriers to find a local solution of a non-convex non-smooth ...

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Convergence analysis of Padé approximations for Helmholtz frequency response problems

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We present new results concerning the approximation of the total variation,

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On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems

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Safe non-smooth black-box optimization with application to policy search

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Statistical Aspects of Wasserstein Distances

Exact Diffusion for Distributed Optimization and Learning-Part II: Convergence Analysis

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

Dynamic Average Diffusion With Randomized Coordinate Updates

Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Convergence analysis of Padé approximations for Helmholtz frequency response problems

Non-local Functionals Related to the Total Variation and Connections with Image Processing

Statistical Aspects of Wasserstein Distances

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Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Avoidance of Convex and Concave Obstacles with Convergence ensured through Contraction

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Performance limits of stochastic sub-gradient learning, Part II: Multi-agent case

Convergence analysis of Padé approximations for Helmholtz frequency response problems

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Safe non-smooth black-box optimization with application to policy search