Nabeel Seedat

Nabeel Seedat

Cornell University

H-index: 7

North America-United States

About Nabeel Seedat

Nabeel Seedat, With an exceptional h-index of 7 and a recent h-index of 7 (since 2020), a distinguished researcher at Cornell University, specializes in the field of Machine Learning, Uncertainty Quantification, Data-Centric AI, Generative Models, AI in Healthcare.

His recent articles reflect a diverse array of research interests and contributions to the field:

Large Language Models to Enhance Bayesian Optimization

DAGnosis: Localized Identification of Data Inconsistencies using Structures

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

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

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

Automated remote sleep monitoring needs machine learning with uncertainty quantification

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

TRIAGE: Characterizing and auditing training data for improved regression

Nabeel Seedat Information

University

Position

/ University of the Witwatersrand

Citations(all)

130

Citations(since 2020)

129

Cited By

17

hIndex(all)

7

hIndex(since 2020)

7

i10Index(all)

4

i10Index(since 2020)

4

Email

University Profile Page

Google Scholar

Nabeel Seedat Skills & Research Interests

Machine Learning

Uncertainty Quantification

Data-Centric AI

Generative Models

AI in Healthcare

Top articles of Nabeel Seedat

Large Language Models to Enhance Bayesian Optimization

arXiv preprint arXiv:2402.03921

2024/2/6

Nabeel Seedat
Nabeel Seedat

H-Index: 3

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

DAGnosis: Localized Identification of Data Inconsistencies using Structures

arXiv preprint arXiv:2402.17599

2024/2/26

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

Computers in Biology and Medicine

2024/2/23

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

arXiv preprint arXiv:2312.12112

2023/12/19

Nabeel Seedat
Nabeel Seedat

H-Index: 3

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

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

2023/12/22

Automated remote sleep monitoring needs machine learning with uncertainty quantification

2023/12/8

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

2024/5

TRIAGE: Characterizing and auditing training data for improved regression

2023/10/29

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

NeurIPS 2023 (37th Conference on Neural Information Processing Systems)

2023/10/25

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

Advances in Neural Information Processing Systems 36

2023/10/25

Dissecting sample hardness: Fine-grained analysis of Hardness Characterization Methods

2023/10/13

Quantile-Free Regression: A Flexible Alternative to Quantile Regression

2023/10/13

Nabeel Seedat
Nabeel Seedat

H-Index: 3

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

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

2023/11/2

Differentiable and transportable structure learning

2023/7/3

Improving Adaptive Conformal Prediction Using Self-Supervised Learning

International Conference on Artificial Intelligence and Statistics (AISTATS)

2023/2/23

Dc-check: A data-centric ai checklist to guide the development of reliable machine learning systems

IEEE Transactions on Artificial Intelligence

2022/11/9

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

2022/10/24

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

2022/2/17

Nabeel Seedat
Nabeel Seedat

H-Index: 3

Mihaela Van Der Schaar
Mihaela Van Der Schaar

H-Index: 42

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

ICML 2022

2022/6/16

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

arXiv preprint arXiv:2205.14761

2022/5/29

Nabeel Seedat
Nabeel Seedat

H-Index: 3

Julia Ive
Julia Ive

H-Index: 5

See List of Professors in Nabeel Seedat University(Cornell University)

Co-Authors

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