6 citations · 10 across the 7 of their papers we have counts for
7 papers
ConceptTracer: Interactive Analysis of Concept Saliency and Selectivity in Neural Representations
Ricardo Knauer, Andre Beinrucker, Erik Rodner
Neural networks deliver impressive predictive performance across a variety of tasks, but they are often opaque in their decision-making processes. Despite a growing interest in mec…
Embedding World Knowledge into Tabular Models: Towards Best Practices for Embedding Pipeline Design
Oksana Kolomenko, Ricardo Knauer, Erik Rodner
Embeddings are a powerful way to enrich data-driven machine learning models with the world knowledge of large language models (LLMs). Yet, there is limited evidence on how to desig…
In Search of Grandmother Cells: Tracing Interpretable Neurons in Tabular Representations
Ricardo Knauer, Erik Rodner
Foundation models are powerful yet often opaque in their decision-making. A topic of continued interest in both neuroscience and artificial intelligence is whether some neurons beh…
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
Ricardo Knauer, Marvin Grimm, Erik Rodner
In practice, we are often faced with small-sized tabular data. However, current tabular benchmarks are not geared towards data-scarce applications, making it very difficult to deri…
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger +5
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their co…
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications
Ricardo Knauer, Erik Rodner
Many industry verticals are confronted with small-sized tabular data. In this low-data regime, it is currently unclear whether the best performance can be expected from simple base…
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
Ricardo Knauer, Erik Rodner
A key challenge in machine learning is to design interpretable models that can reduce their inputs to the best subset for making transparent predictions, especially in the clinical…