18 citations · 53 across the 11 of their papers we have counts for
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cs.LG2023★ 1 cited
Quantification of Predictive Uncertainty via Inference-Time Sampling
Katarína Tóthová, Ľubor Ladický, Daniel Thul +2
Predictive variability due to data ambiguities has typically been addressed via construction of dedicated models with built-in probabilistic capabilities that are trained to predic…
cs.LG2023★ 8 cited
FedFA: Federated Feature Augmentation
Tianfei Zhou, Ender Konukoglu
Federated learning is a distributed paradigm that allows multiple parties to collaboratively train deep models without exchanging the raw data. However, the data distribution among…
cs.LG2014★ 8 cited
Approximate False Positive Rate Control in Selection Frequency for Random Forest
Ender Konukoglu, Melanie Ganz
Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in n…