Area of research
Artificial Intelligence · Computational Theory and Mathematics
Research interest
Research interests include Machine Learning and Data Classification, Machine Learning and Algorithms, Advanced Multi-Objective Optimization Algorithms, and Advanced Neural Network Applications.
Accurate predictions on small data with a tabular foundation model.
DAFT: Data-Aware Fine-Tuning of Foundation Models for Efficient and Effective Medical Image Segmentation
Practitioner Motives to Use Different Hyperparameter Optimization Methods
AI-Driven Regression Techniques for Modeling Mobile Network Technology (6G) Using TensorFlow-Keras Framework
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
AMLTK: A Modular AutoML Toolkit in Python
RNA-Protein Interaction Classification via Sequence Embeddings
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL.
Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases
Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019.
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA
Bayesian Optimization in a Billion Dimensions via Random Embeddings
Algorithm runtime prediction: Methods & evaluation