278 citations · 832 across the 38 of their papers we have counts for
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Scalable Adaptive Stochastic Optimization Using Random Projections
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher +2
Adaptive stochastic gradient methods such as AdaGrad have gained popularity in particular for training deep neural networks. The most commonly used and studied variant maintains a…
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes
Nico S. Gorbach, Stefan Bauer, Joachim M. Buhmann
Gradient matching with Gaussian processes is a promising tool for learning parameters of ordinary differential equations (ODE's). The essence of gradient matching is to model the p…
Model Selection for Gaussian Process Regression by Approximation Set Coding
Benjamin Fischer, Nico Gorbach, Stefan Bauer +2
Gaussian processes are powerful, yet analytically tractable models for supervised learning. A Gaussian process is characterized by a mean function and a covariance function (kernel…
Greedy MAXCUT Algorithms and their Information Content
Yatao Bian, Alexey Gronskiy, Joachim M. Buhmann
MAXCUT defines a classical NP-hard problem for graph partitioning and it serves as a typical case of the symmetric non-monotone Unconstrained Submodular Maximization (USM) problem.…
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains
Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann +1
Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate…
Multi-Organ Cancer Classification and Survival Analysis
Stefan Bauer, Nicolas Carion, Peter Schüffler +3
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require…