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2020 – today
- 2024
[j3]Elisabeth R. M. Heremans
, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos:
U-PASS: An uncertainty-guided deep learning pipeline for automated sleep staging. Comput. Biol. Medicine 171: 108205 (2024)- [j2]Lea Goetz, Nabeel Seedat, Robert Vandersluis, Mihaela van der Schaar:
Generalization - a key challenge for responsible AI in patient-facing clinical applications. npj Digit. Medicine 7(1) (2024) - [j1]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. IEEE Trans. Artif. Intell. 5(6): 2589-2603 (2024) - [c16]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. AISTATS 2024: 1864-1872 - [c15]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. ICLR 2024 - [c14]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. ICLR 2024 - [c13]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. ICML 2024 - [c12]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes. ICML 2024 - [i23]Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar:
Large Language Models to Enhance Bayesian Optimization. CoRR abs/2402.03921 (2024) - [i22]Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
DAGnosis: Localized Identification of Data Inconsistencies using Structures. CoRR abs/2402.17599 (2024) - [i21]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI. CoRR abs/2403.04551 (2024) - [i20]Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela van der Schaar:
Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise. CoRR abs/2406.03258 (2024) - [i19]Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar:
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling. CoRR abs/2406.13733 (2024) - 2023
- [c11]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. AISTATS 2023: 10160-10177 - [c10]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. ICML 2023: 2206-2233 - [c9]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. NeurIPS 2023 - [c8]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. NeurIPS 2023 - [c7]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. NeurIPS 2023 - [c6]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization. NeurIPS 2023 - [i18]Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela van der Schaar:
Improving Adaptive Conformal Prediction Using Self-Supervised Learning. CoRR abs/2302.12238 (2023) - [i17]Elisabeth R. M. Heremans, Nabeel Seedat, Bertien Buyse, Dries Testelmans, Mihaela van der Schaar, Maarten De Vos:
U-PASS: an Uncertainty-guided deep learning Pipeline for Automated Sleep Staging. CoRR abs/2306.04663 (2023) - [i16]Boris van Breugel, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data. CoRR abs/2310.16524 (2023) - [i15]Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic:
Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmark. CoRR abs/2310.16981 (2023) - [i14]Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar:
TRIAGE: Characterizing and auditing training data for improved regression. CoRR abs/2310.18970 (2023) - [i13]Hao Sun, Alex J. Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar:
When is Off-Policy Evaluation Useful? A Data-Centric Perspective. CoRR abs/2311.14110 (2023) - [i12]Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar:
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in ultra low-data regimes. CoRR abs/2312.12112 (2023) - 2022
- [c5]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. ICML 2022: 19467-19496 - [c4]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. ICML 2022: 19497-19521 - [c3]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. NeurIPS 2022 - [i11]Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar:
Data-SUITE: Data-centric identification of in-distribution incongruous examples. CoRR abs/2202.08836 (2022) - [i10]Hongshu Liu, Nabeel Seedat, Julia Ive:
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing. CoRR abs/2205.14761 (2022) - [i9]Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
Differentiable and Transportable Structure Learning. CoRR abs/2206.06354 (2022) - [i8]Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela van der Schaar:
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations. CoRR abs/2206.08311 (2022) - [i7]Hao Sun, Boris van Breugel, Jonathan Crabbé, Nabeel Seedat, Mihaela van der Schaar:
DAUX: a Density-based Approach for Uncertainty eXplanations. CoRR abs/2207.05161 (2022) - [i6]Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar:
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data. CoRR abs/2210.13043 (2022) - [i5]Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar:
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems. CoRR abs/2211.05764 (2022) - 2020
- [c2]Nabeel Seedat, Vered Aharonson, Ilana Schlesinger:
Automated machine vision enabled detection of movement disorders from hand drawn spirals. ICHI 2020: 1-5 - [i4]Nabeel Seedat, Vered Aharonson:
Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data. CoRR abs/2006.12094 (2020) - [i3]Nabeel Seedat, Vered Aharonson, Ilana Schlesinger:
Automated machine vision enabled detection of movement disorders from hand drawn spirals. CoRR abs/2006.12121 (2020) - [i2]Nabeel Seedat:
MCU-Net: A framework towards uncertainty representations for decision support system patient referrals in healthcare contexts. CoRR abs/2007.03995 (2020)
2010 – 2019
- 2019
- [i1]Nabeel Seedat, Christopher Kanan:
Towards calibrated and scalable uncertainty representations for neural networks. CoRR abs/1911.00104 (2019) - 2018
- [c1]Nabeel Seedat, Irfaan Mohamed, Abdul-Khaaliq Mohamed:
Custom Force Sensor and Sensory Feedback System to Enable Grip Control of a Robotic Prosthetic Hand. BioRob 2018: 248-253
Coauthor Index
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last updated on 2024-10-07 22:21 CEST by the dblp team
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