13 citations · 31 across the 8 of their papers we have counts for
8 papers
Large Language Models to Enhance Bayesian Optimization
Tennison Liu, Nicolás Astorga, Nabeel Seedat +1
Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably…
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
Characterizing samples that are difficult to learn from is crucial to developing highly performant ML models. This has led to numerous Hardness Characterization Methods (HCMs) that…
DAGnosis: Localized Identification of Data Inconsistencies using Structures
Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat +3
Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models. While recent data-centric methods are able…
TRIAGE: Characterizing and auditing training data for improved regression
Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian +1
Data quality is crucial for robust machine learning algorithms, with the recent interest in data-centric AI emphasizing the importance of training data characterization. However, c…
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark
Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar +1
Synthetic data serves as an alternative in training machine learning models, particularly when real-world data is limited or inaccessible. However, ensuring that synthetic data mir…
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data
Boris van Breugel, Nabeel Seedat, Fergus Imrie +1
Evaluating the performance of machine learning models on diverse and underrepresented subgroups is essential for ensuring fairness and reliability in real-world applications. Howev…