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Nicolas Henri Bernard Flammarion

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Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Nicolas Henri Bernard Flammarion

We consider minimizing a nonconvex, smooth function

f

on a Riemannian manifold

\mathcal{M}

. We show that a perturbed version of Riemannian gradient descent algorithm converges to a second-order stationary point (and hence is able to escape saddle point ...

2019

Nicolas Henri Bernard Flammarion

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Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

RobustBench: a standardized adversarial robustness benchmark

Is There an Analog of Nesterov Acceleration for MCMC?

Escaping from saddle points on Riemannian manifolds

Sampling can be faster than optimization

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Optimal rates of statistical seriation

Fast Mean Estimation with Sub-Gaussian Rates

On the theory of variance reduction for stochastic gradient monte carlo

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation

Sampling can be faster than optimization

Optimal rates of statistical seriation

Gen-Oja: A Simple and Efficient Algorithm for Streaming Generalized Eigenvector Computation

Is There an Analog of Nesterov Acceleration for MCMC?

RobustBench: a standardized adversarial robustness benchmark

Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Escaping from saddle points on Riemannian manifolds

On the theory of variance reduction for stochastic gradient monte carlo

Improved Bounds for Discretization of Langevin Diffusions: Near-Optimal Rates without Convexity

Fast Mean Estimation with Sub-Gaussian Rates