Sebastian Urban Stich

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Related publications (27)

Sparsified SGD with Memory

Jean-Baptiste Francis Marie Juliette Cordonnier, Martin Jaggi, Sebastian Urban Stich

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The communication overhead is a key bottleneck that hinders perfect scalability. ...

2018

Adaptive balancing of gradient and update computation times using global geometry and approximate subproblems

Sai Praneeth Reddy Karimireddy, Martin Jaggi, Sebastian Urban Stich

2018

Stochastic Variance Reduced Gradient Optimization of Generative Adversarial Networks

François Fleuret, Tatjana Chavdarova, Martin Jaggi, Sebastian Urban Stich

2018

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

Sebastian Urban Stich

We present the first accelerated randomized algorithm for solving linear systems in Euclidean spaces. One essential problem of this type is the matrix inversion problem. In particular, our algorithm can be specialized to invert positive definite matrices i ...

2018

On Matching Pursuit and Coordinate Descent

Sai Praneeth Reddy Karimireddy, Martin Jaggi, Sebastian Urban Stich, Anant Raj

Two popular examples of first-order optimization methods over linear spaces are coordinate descent and matching pursuit algorithms, with their randomized variants. While the former targets the optimization by moving along coordinates, the latter considers ...

2018

Safe Adaptive Importance Sampling

Martin Jaggi, Sebastian Urban Stich, Anant Raj

Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - ...

2017

Approximate Steepest Coordinate Descent

Martin Jaggi, Sebastian Urban Stich, Anant Raj

We propose a new selection rule for the coordinate selection in coordinate descent methods for huge-scale optimization. The efficiency of this novel scheme is provably better than the efficiency of uniformly random selection, and can reach the efficiency o ...

PMLR2017

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