65 citations · 159 across the 23 of their papers we have counts for
24 papers
Bayesian Optimization of CVaR with Unknown Hyperparameters
Valentin Margraf, Jonas Hanselle, Viktor Bengs +3
We study Bayesian optimization of the Conditional Value-at-Risk (CVaR) of an expensive black-box function that depends on a decision and an environmental variable. Existing no-regr…
Evolutionary Mapping of Neural Networks to Spatial Accelerators
Alessandro Pierro, Jonathan Timcheck, Jason Yik +3
Spatial accelerators, composed of arrays of compute-memory integrated units, offer an attractive platform for deploying inference workloads with low latency and low energy consumpt…
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
Valentin Margraf, Marcel Wever, Sandra Gilhuber +3
In settings where only a budgeted amount of labeled data can be afforded, active learning seeks to devise query strategies for selecting the most informative data points to be labe…
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger +7
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to un…
Automated Machine Learning for Multi-Label Classification
Marcel Wever
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand. For sup…
Information Leakage Detection through Approximate Bayes-optimal Prediction
Pritha Gupta, Marcel Wever, Eyke Hüllermeier
In today's data-driven world, the proliferation of publicly available information raises security concerns due to the information leakage (IL) problem. IL involves unintentionally…