19 citations · 82 across the 39 of their papers we have counts for
27 papers · 1 filter
Influence-Guided Symbolic Regression: Scientific Discovery via LLM-Driven Equation Search with Granular Feedback
Evgeny S. Saveliev, Samuel Holt, Nabeel Seedat +3
Large Language Models (LLMs) offer a promising avenue for scientific discovery, yet their application to symbolic regression is often constrained by inefficient search strategies a…
What's the next frontier for Data-centric AI? Data Savvy Agents
Nabeel Seedat, Jiashuo Liu, Mihaela van der Schaar
The recent surge in AI agents that autonomously communicate, collaborate with humans and use diverse tools has unlocked promising opportunities in various real-world settings. Howe…
Towards Human-Guided, Data-Centric LLM Co-Pilots
Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat +2
Machine learning (ML) has the potential to revolutionize various domains, but its adoption is often hindered by the disconnect between the needs of domain experts and translating t…
Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding
Nabeel Seedat, Caterina Tozzi, Andrea Hita Ardiaca +3
The reuse of historical clinical trial data has significant potential to accelerate medical research and drug development. However, interoperability challenges, particularly with m…
Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments
Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk +1
Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches…
Matchmaker: Self-Improving Large Language Model Programs for Schema Matching
Nabeel Seedat, Mihaela van der Schaar
Schema matching -- the task of finding matches between attributes across disparate data sources with different tables and hierarchies -- is critical for creating interoperable mach…
Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models
Paulius Rauba, Nabeel Seedat, Max Ruiz Luyten +1
The predominant de facto paradigm of testing ML models relies on either using only held-out data to compute aggregate evaluation metrics or by assessing the performance on differen…
You can't handle the (dirty) truth: Data-centric insights improve pseudo-labeling
Nabeel Seedat, Nicolas Huynh, Fergus Imrie +1
Pseudo-labeling is a popular semi-supervised learning technique to leverage unlabeled data when labeled samples are scarce. The generation and selection of pseudo-labels heavily re…
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…
Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes
Nabeel Seedat, Nicolas Huynh, Boris van Breugel +1
Machine Learning (ML) in low-data settings remains an underappreciated yet crucial problem. Hence, data augmentation methods to increase the sample size of datasets needed for ML a…
When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective
Hao Sun, Alex J. Chan, Nabeel Seedat +2
Evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. On the one hand, it brings opportunities for safe policy improvement u…
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…
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Nabeel Seedat, Alan Jeffares, Fergus Imrie +1
Conformal prediction is a powerful distribution-free tool for uncertainty quantification, establishing valid prediction intervals with finite-sample guarantees. To produce valid in…
DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems
Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar
While there have been a number of remarkable breakthroughs in machine learning (ML), much of the focus has been placed on model development. However, to truly realize the potential…
Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data
Nabeel Seedat, Jonathan Crabbé, Ioana Bica +1
High model performance, on average, can hide that models may systematically underperform on subgroups of the data. We consider the tabular setting, which surfaces the unique issue…
What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization
Hao Sun, Boris van Breugel, Jonathan Crabbe +2
Uncertainty Quantification (UQ) is essential for creating trustworthy machine learning models. Recent years have seen a steep rise in UQ methods that can flag suspicious examples,…
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat, Fergus Imrie, Alexis Bellot +2
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal infer…
Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing
Hongshu Liu, Nabeel Seedat, Julia Ive
Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts. This is especially tr…
Differentiable and Transportable Structure Learning
Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie +1
Directed acyclic graphs (DAGs) encode a lot of information about a particular distribution in their structure. However, compute required to infer these structures is typically supe…
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
Systematic quantification of data quality is critical for consistent model performance. Prior works have focused on out-of-distribution data. Instead, we tackle an understudied yet…