63 citations · 244 across the 40 of their papers we have counts for
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Deep Pipeline Embeddings for AutoML
Sebastian Pineda Arango, Josif Grabocka
Automated Machine Learning (AutoML) is a promising direction for democratizing AI by automatically deploying Machine Learning systems with minimal human expertise. The core technic…
Interpretable Mesomorphic Networks for Tabular Data
Arlind Kadra, Sebastian Pineda Arango, Josif Grabocka
Even though neural networks have been long deployed in applications involving tabular data, still existing neural architectures are not explainable by design. In this paper, we pro…
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks
Mofassir ul Islam Arif, Mohsan Jameel, Josif Grabocka +1
The strength of machine learning models stems from their ability to learn complex function approximations from data; however, this strength also makes training deep neural networks…
Deep Ranking Ensembles for Hyperparameter Optimization
Abdus Salam Khazi, Sebastian Pineda Arango, Josif Grabocka
Automatically optimizing the hyperparameters of Machine Learning algorithms is one of the primary open questions in AI. Existing work in Hyperparameter Optimization (HPO) trains su…
Scaling Laws for Hyperparameter Optimization
Arlind Kadra, Maciej Janowski, Martin Wistuba +1
Hyperparameter optimization is an important subfield of machine learning that focuses on tuning the hyperparameters of a chosen algorithm to achieve peak performance. Recently, the…