Publications associées à Stochastic Spectral Descent for Restricted Boltzmann Machines.

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Convergence of adaptive algorithms for constrained weakly convex optimization

Volkan Cevher, Ahmet Alacaoglu, Yurii Malitskyi

We analyze the adaptive first order algorithm AMSGrad, for solving a constrained stochastic optimization problem with a weakly convex objective. We prove the

\tilde O(t^{-1/2})

rate of convergence for the squared norm of the gradient of Moreau envelope, ...

2021

Stochastic Spectral Descent for Restricted Boltzmann Machines.

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Convergence of adaptive algorithms for constrained weakly convex optimization

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

Extrapolation for Large-batch Training in Deep Learning

Second-Order Guarantees In Federated Learning

Multi-armed Bandits in Action

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Local SGD Converges Fast and Communicates Little

Diffusion Learning In Non-Convex Environments

Learning Positive Functions with Pseudo Mirror Descent

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Multi-armed Bandits in Action

SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient

Convergence of adaptive algorithms for constrained weakly convex optimization

Diffusion Learning In Non-Convex Environments

Learning Positive Functions with Pseudo Mirror Descent

Local SGD Converges Fast and Communicates Little

Second-Order Guarantees In Federated Learning

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Extrapolation for Large-batch Training in Deep Learning

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem