Gaussian Processes for Missing Value Imputation

Jafrasteh, Bahram; Hernández-Lobato, Daniel; Lubián‐López, Simón Pedro; Benavente-Fernánde, Isabel

doi:10.2139/ssrn.4170143

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Naive zero or mean value (Wen et al 2021;Hu and Chen 2018) padding is of minimal exertion, yet can be quite inaccurate. More advanced statistical techniques primarily rely on the nonparametric Bayesian approach (Manrique-Vallier and Reiter 2017), k-nearest neighbors (Jadhav, Pramod, and Ramanathan 2019), Gaussian processes (Jafrasteh et al 2023). Current prevalent models building upon deep learning are more capable of exploring the correlation between modalities to impute the missing ones.…”

Section: Introductionmentioning

confidence: 99%

RedCore: Relative Advantage Aware Cross-Modal Representation Learning for Missing Modalities with Imbalanced Missing Rates

Sun,

Zhang,

Han

et al. 2024

AAAI

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Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the incomplete samples while guaranteeing that they are properly supervised? 2) when the missing rates of different modalities vary, causing or exacerbating the imbalance among modalities, how to address the imbalance and ensure all modalities are well-trained. To tackle these two challenges, we first introduce the variational information bottleneck (VIB) method for the cross-modal representation learning of missing modalities, which capitalizes on the available modalities and the labels as supervision. Then, accounting for the imbalanced missing rates, we define relative advantage to quantify the advantage of each modality over others. Accordingly, a bi-level optimization problem is formulated to adaptively regulate the supervision of all modalities during training. As a whole, the proposed approach features Relative advantage aware Cross-modal representation learning (abbreviated as RedCore) for missing modalities with imbalanced missing rates. Extensive empirical results demonstrate that RedCore outperforms competing models in that it exhibits superior robustness against either large or imbalanced missing rates. The code is available at: https://github.com/sunjunaimer/RedCore.

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Section: Introductionmentioning

confidence: 99%

RedCore: Relative Advantage Aware Cross-Modal Representation Learning for Missing Modalities with Imbalanced Missing Rates

Sun,

Zhang,

Han

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

Faster Imputation Using Singular Value Decomposition for Sparse Data

Nguyen,

Tran,

et al. 2023

Lecture Notes in Computer Science

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Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations

Janssen¹,

Bennis

Mathôt³

2022

Pharmaceutics

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Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology, pharmacology, and disease to describe and quantify the interactions between medication and patient. As these models become more and more advanced, the need for advanced data analysis tools grows. Recently, there has been much interest in the adoption of machine learning (ML) algorithms. These algorithms offer strong function approximation capabilities and might reduce the time spent on model development. However, ML tools are not yet an integral part of the pharmacometrics workflow. The goal of this work is to discuss how ML algorithms have been applied in four stages of the pharmacometrics pipeline: data preparation, hypothesis generation, predictive modelling, and model validation. We will also discuss considerations before the use of ML algorithms with respect to each topic. We conclude by summarizing applications that hold potential for adoption by pharmacometricians.

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Gaussian Processes for Missing Value Imputation

Cited by 3 publications

References 18 publications

RedCore: Relative Advantage Aware Cross-Modal Representation Learning for Missing Modalities with Imbalanced Missing Rates

RedCore: Relative Advantage Aware Cross-Modal Representation Learning for Missing Modalities with Imbalanced Missing Rates

Faster Imputation Using Singular Value Decomposition for Sparse Data

Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations

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