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no code implementations • 16 Nov 2021 • Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, Anima Anandkumar

In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents.

1 code implementation • 25 Oct 2021 • Jiawei Zhao, Florian Schäfer, Anima Anandkumar

Deep neural networks are usually initialized with random weights, with adequately selected initial variance to ensure stable signal propagation during training.

3 code implementations • 17 Jun 2020 • Florian Schäfer, Anima Anandkumar, Houman Owhadi

Finally, we obtain the next iterate by following this direction according to the dual geometry induced by the Bregman potential.

1 code implementation • 29 Apr 2020 • Florian Schäfer, Matthias Katzfuss, Houman Owhadi

We propose to compute a sparse approximate inverse Cholesky factor $L$ of a dense covariance matrix $\Theta$ by minimizing the Kullback-Leibler divergence between the Gaussian distributions $\mathcal{N}(0, \Theta)$ and $\mathcal{N}(0, L^{-\top} L^{-1})$, subject to a sparsity constraint.

Numerical Analysis Numerical Analysis Optimization and Control Statistics Theory Computation Statistics Theory

2 code implementations • ICML 2020 • Florian Schäfer, Hongkai Zheng, Anima Anandkumar

We show that opponent-aware modelling of generator and discriminator, as present in competitive gradient descent (CGD), can significantly strengthen ICR and thus stabilize GAN training without explicit regularization.

7 code implementations • NeurIPS 2019 • Florian Schäfer, Anima Anandkumar

We introduce a new algorithm for the numerical computation of Nash equilibria of competitive two-player games.

3 code implementations • 20 Sep 2017 • Leon Thurner, Alexander Scheidler, Florian Schäfer, Jan-Hendrik Menke, Julian Dollichon, Friederike Meier, Steffen Meinecke, Martin Braun

pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems.

Computational Engineering, Finance, and Science

1 code implementation • 7 Jun 2017 • Florian Schäfer, T. J. Sullivan, Houman Owhadi

This block-factorisation can provably be obtained in complexity $\mathcal{O} ( N \log( N ) \log^{d}( N /\epsilon) )$ in space and $\mathcal{O} ( N \log^{2}( N ) \log^{2d}( N /\epsilon) )$ in time.

Numerical Analysis Computational Complexity Data Structures and Algorithms Probability 65F30, 42C40, 65F50, 65N55, 65N75, 60G42, 68Q25, 68W40

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