9.4k citations · 16.5k across the 107 of their papers we have counts for
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Squirrel: A Switching Hyperparameter Optimizer
Noor Awad, Gresa Shala, Difan Deng +9
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge. Motivated by the fact that different optimizers work well on different problems, our approach swit…
Differential Evolution for Neural Architecture Search
Noor Awad, Neeratyoy Mallik, Frank Hutter
Neural architecture search (NAS) methods rely on a search strategy for deciding which architectures to evaluate next and a performance estimation strategy for assessing their perfo…
Convergence Analysis of Homotopy-SGD for non-convex optimization
Matilde Gargiani, Andrea Zanelli, Quoc Tran-Dinh +2
First-order stochastic methods for solving large-scale non-convex optimization problems are widely used in many big-data applications, e.g. training deep neural networks as well as…
Hyperparameter Transfer Across Developer Adjustments
Danny Stoll, Jörg K. H. Franke, Diane Wagner +2
After developer adjustments to a machine learning (ML) algorithm, how can the results of an old hyperparameter optimization (HPO) automatically be used to speedup a new HPO? This q…
On the Importance of Domain Model Configuration for Automated Planning Engines
Mauro Vallati, Lukas Chrpa, Thomas L. McCluskey +1
The development of domain-independent planners within the AI Planning community is leading to "off-the-shelf" technology that can be used in a wide range of applications. Moreover,…
Smooth Variational Graph Embeddings for Efficient Neural Architecture Search
Jovita Lukasik, David Friede, Arber Zela +2
Neural architecture search (NAS) has recently been addressed from various directions, including discrete, sampling-based methods and efficient differentiable approaches. While the…