Seedat, Nabeel

25 publications

ICML 2025 Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching Nabeel Seedat, Mihaela Van Der Schaar

ICLR 2025 Going Beyond Static: Understanding Shifts with Time-Series Attribution Jiashuo Liu, Nabeel Seedat, Peng Cui, Mihaela van der Schaar

ICLRW 2025 Position: What's the Next Frontier for Data-Centric AI? Data Savvy Agents! Nabeel Seedat, Jiashuo Liu, Mihaela van der Schaar

ICLRW 2025 Towards Human-Guided, Data-Centric LLM Co-Pilots Evgeny Saveliev, Jiashuo Liu, Nabeel Seedat, Anders Boyd, Mihaela van der Schaar

NeurIPS 2024 Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios Nicolás Astorga, Tennison Liu, Nabeel Seedat, Mihaela van der Schaar

NeurIPS 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

ICML 2024 Curated LLM: Synergy of LLMs and Data Curation for Tabular Augmentation in Low-Data Regimes Nabeel Seedat, Nicolas Huynh, Boris Breugel, Mihaela Schaar

AISTATS 2024 DAGnosis: Localized Identification of Data Inconsistencies Using Structures Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela Schaar

ICLR 2024 Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI Nabeel Seedat, Fergus Imrie, Mihaela van der Schaar

ICLR 2024 Large Language Models to Enhance Bayesian Optimization Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar

NeurIPSW 2024 Matchmaker: Self-Improving Compositional LLM Programs for Table Schema Matching Nabeel Seedat, Mihaela van der Schaar

NeurIPSW 2024 Matchmaker: Self-Improving Large Language Model Programs for Schema Matching Nabeel Seedat, Mihaela van der Schaar

ICML 2024 Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise Thomas Pouplin, Alan Jeffares, Nabeel Seedat, Mihaela Van Der Schaar

NeurIPS 2024 Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments Paulius Rauba, Nabeel Seedat, Krzysztof Kacprzyk, Mihaela van der Schaar

DMLR 2024 When Is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? a Data-Centric Perspective Hao Sun, Alex James Chan, Nabeel Seedat, Alihan Hüyük, Mihaela van der Schaar

DMLR 2024 You Can't Handle the (dirty) Truth: Data-Centric Insights Improve Pseudo-Labeling Nabeel Seedat, Nicolas Huynh, Fergus Imrie, Mihaela van der Schaar

NeurIPS 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

ICML 2023 Differentiable and Transportable Structure Learning Jeroen Berrevoets, Nabeel Seedat, Fergus Imrie, Mihaela Van Der Schaar

AISTATS 2023 Improving Adaptive Conformal Prediction Using Self-Supervised Learning Nabeel Seedat, Alan Jeffares, Fergus Imrie, Mihaela Schaar

NeurIPS 2023 Reimagining Synthetic Tabular Data Generation Through Data-Centric AI: A Comprehensive Benchmark Lasse Hansen, Nabeel Seedat, Mihaela van der Schaar, Andrija Petrovic

NeurIPS 2023 TRIAGE: Characterizing and Auditing Training Data for Improved Regression Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

NeurIPS 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

ICML 2022 Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations Nabeel Seedat, Fergus Imrie, Alexis Bellot, Zhaozhi Qian, Mihaela Schaar

NeurIPS 2022 Data-IQ: Characterizing Subgroups with Heterogeneous Outcomes in Tabular Data Nabeel Seedat, Jonathan Crabbé, Ioana Bica, Mihaela van der Schaar

ICML 2022 Data-SUITE: Data-Centric Identification of In-Distribution Incongruous Examples Nabeel Seedat, Jonathan Crabbé, Mihaela Schaar