28 citations · 44 across the 7 of their papers we have counts for
5 papers · 1 filter
TraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attribution
Tommaso Bendinelli, Artur Dox, Christian Holz
LLM agents are increasingly applied to anomaly detection and root-cause analysis in time-series observations collected from real-world systems; however, their performance on these…
Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets
Tommaso Bendinelli, Artur Dox, Christian Holz
High-quality, error-free datasets are a key ingredient in building reliable, accurate, and unbiased machine learning (ML) models. However, real world datasets often suffer from err…
Controllable Neural Symbolic Regression
Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variable…
Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction
Luca Biggio, Tommaso Bendinelli, Chetan Kulkarni +1
Electrochemical batteries are ubiquitous devices in our society. When they are employed in mission-critical applications, the ability to precisely predict the end of discharge unde…
Neural Symbolic Regression that Scales
Luca Biggio, Tommaso Bendinelli, Alexander Neitz +2
Symbolic equations are at the core of scientific discovery. The task of discovering the underlying equation from a set of input-output pairs is called symbolic regression. Traditio…