Nabeel Seedat

According to our database, Nabeel Seedat authored at least 33 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

IEEE Trans. Artif. Intell., June, 2024

Generalization - a key challenge for responsible AI in patient-facing clinical applications.

Lea Goetz, Nabeel Seedat, Robert Vandersluis, Mihaela van der Schaar

npj Digit. Medicine, 2024

Modeling disagreement in automatic data labeling for semi-supervised learning in Clinical Natural Language Processing.

Hongshu Liu, Nabeel Seedat, Julia Ive

Frontiers Artif. Intell., 2024

Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding.

Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca, Mihaela van der Schaar, James Weatherall, Adam Taylor

CoRR, 2024

Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments.

Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar

CoRR, 2024

Matchmaker: Self-Improving Large Language Model Programs for Schema Matching.

Nabeel Seedat, Mihaela van der Schaar

CoRR, 2024

Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models.

Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten, Mihaela van der Schaar

CoRR, 2024

You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling.

Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

CoRR, 2024

U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging.

Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos

Comput. Biol. Medicine, 2024

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes.

Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise.

Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Large Language Models to Enhance Bayesian Optimization.

Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar

Proceedings of the Twelfth International Conference on Learning Representations, 2024

DAGnosis: Localized Identification of Data Inconsistencies using Structures.

Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes.

Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar

CoRR, 2023

When is Off-Policy Evaluation Useful? A Data-Centric Perspective.

Hao Sun, Alex J. Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar

CoRR, 2023

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization.

Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

TRIAGE: Characterizing and auditing training data for improved regression.

Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark.

Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data.

Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Differentiable and Transportable Structure Learning.

Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2023

Improving Adaptive Conformal Prediction Using Self-Supervised Learning.

Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems.

Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

CoRR, 2022

DAUX: a Density-based Approach for Uncertainty eXplanations.

Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar

CoRR, 2022

Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing.

Hongshu Liu, Nabeel Seedat, Julia Ive

CoRR, 2022

Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data.

Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations.

Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

Data-SUITE: Data-centric identification of in-distribution incongruous examples.

Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar

Proceedings of the International Conference on Machine Learning, 2022

2020

MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts.

Nabeel Seedat

CoRR, 2020

Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data.

Nabeel Seedat, Vered Aharonson

CoRR, 2020

Automated machine vision enabled detection of movement disorders from hand drawn spirals.

Nabeel Seedat, Vered Aharonson, Ilana Schlesinger

Proceedings of the 8th IEEE International Conference on Healthcare Informatics, 2020

2019

Towards calibrated and scalable uncertainty representations for neural networks.

Nabeel Seedat, Christopher Kanan

CoRR, 2019

2018

Custom Force Sensor and Sensory Feedback System to Enable Grip Control of a Robotic Prosthetic Hand.

Nabeel Seedat, Irfaan Mohamed, Abdul-Khaaliq Mohamed

Proceedings of the 7th IEEE International Conference on Biomedical Robotics and Biomechatronics, 2018