43 citations · 44 across the 2 of their papers we have counts for
3 papers
Evaluating Bayes Error Estimators on Real-World Datasets with FeeBee
Cedric Renggli, Luka Rimanic, Nora Hollenstein +1
The Bayes error rate (BER) is a fundamental concept in machine learning that quantifies the best possible accuracy any classifier can achieve on a fixed probability distribution. D…
A Data Quality-Driven View of MLOps
Cedric Renggli, Luka Rimanic, Nezihe Merve Gürel +3
Developing machine learning models can be seen as a process similar to the one established for traditional software development. A key difference between the two lies in the strong…
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
Luka Rimanic, Cedric Renggli, Bo Li +1
The k-Nearest Neighbors (kNN) classifier is a fundamental non-parametric machine learning algorithm. However, it is well known that it suffers from the curse of dimensionality, whi…