DAGnosis: Localized Identification of Data Inconsistencies using Structures

2 code implementations • 26 Feb 2024 • Nicolas Huynh, Jeroen Berrevoets, Nabeel Seedat, Jonathan Crabbé, Zhaozhi Qian, Mihaela van der Schaar

Identification and appropriate handling of inconsistencies in data at deployment time is crucial to reliably use machine learning models.

Time Series Diffusion in the Frequency Domain

1 code implementation • 8 Feb 2024 • Jonathan Crabbé, Nicolas Huynh, Jan Stanczuk, Mihaela van der Schaar

We explain this observation by showing that time series from these datasets tend to be more localized in the frequency domain than in the time domain, which makes them easier to model in the former case.

Denoising Inductive Bias +1

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

no code implementations • 19 Dec 2023 • Nabeel Seedat, Nicolas Huynh, Boris van Breugel, Mihaela van der Schaar

Machine Learning (ML) in low-data settings remains an underappreciated yet crucial problem.

Data Augmentation

Active Preference-Based Gaussian Process Regression for Reward Learning

1 code implementation • 6 May 2020 • Erdem Biyik, Nicolas Huynh, Mykel J. Kochenderfer, Dorsa Sadigh

Our results in simulations and a user study suggest that our approach can efficiently learn expressive reward functions for robotics tasks.

regression