publications

2024

1. ICML

   Minimizing f-Divergences by Interpolating Velocity Fields

   Song Liu, Jiahao Yu, Jack Simons , Mingxuan Yi , and Mark Beaumont

   In Proceedings of the 41st International Conference on Machine Learning , Jul 2024

   Abs arXiv PDF

   Many machine learning problems can be formulated as approximating a target distribution using a particle distribution by minimizing a statistical discrepancy. Wasserstein Gradient Flow can be employed to move particles along a path that minimizes the f-divergence between the target and particle distributions. To perform such movements we need to calculate the corresponding velocity fields which include a density ratio function between these two distributions. While previous works estimated the density ratio function first and then differentiated the estimated ratio, this approach may suffer from overfitting, which leads to a less accurate estimate. Inspired by non-parametric curve fitting, we directly estimate these velocity fields using interpolation. We prove that our method is asymptotically consistent under mild conditions. We validate the effectiveness using novel applications on domain adaptation and missing data imputation.

2. UK-QSAR

   Filling the gaps: Data Imputation Methods for Drug Discovery

   Jiahao Yu, and David Marcus

   In UK-QSAR Spring 2024 Meeting , Apr 2024

   Abs Poster

   Drug discovery datasets are often shown as sparse, noisy, and heterogeneous. To facilitate drug discovery projects and to ensure the effectiveness of Machine Learning (ML) algorithms and predictive models, it is necessary sometimes to find methods that can fill in the gaps in this data. In this poster, we discuss several classic and state-of-the-art data imputation methods and compare their performance with classic QSAR modelling. We found that data imputation models can usually outperform classic QSAR models, however some are unsuitable for data imputation in drug discovery, and some will require extensive calculation time.