Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Han, Beining; Zheng, Chongyi; Chan, Harris; Paster, Keiran; Zhang, Michael R.; Ba, Jimmy

doi:10.48550/arxiv.2110.14248

Cited by 2 publications

(2 citation statements)

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“…Given this interesting setting and the promises of domain generalization in studying machine learning robustness, the community has developed a torrent of methods. Most of the existing methods fall into two categories: one is to build explicit regularization that pushes a model to learn representations that are invariant to the "style" across these domains [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71][72]; the other one is to perform data augmentation that can introduce more diverse data to enrich the data of certain "semantic" information with the "style" from other domains [73][74][75][76][77][78][79][80], and also aims to train a model that is invariant to these "styles". More recently, there has been a line of approaches that aims to distill knowledge from pre-trained models into a smaller model to improve generalization performance [81][82][83][84][85].…”

Section: Domain Generalizationmentioning

confidence: 99%

Toward Out-of-Domain Binding Affinity Prediction

Zhao,

Fang,

Wang

2023

Preprint

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Binding affinity prediction is pivotal in drug design, offering insights into the interactions between ligands and protein targets and thereby significantly influencing the drug development pipeline. Its potential to expedite the identification of drug candidates has led to extensive research focused on developing machine learning algorithms for predicting binding affinity. However, most developments have concentrated on independently and identically distributed (i.i.d) data. In real-world scenarios, prediction models may encounter novel chemical substructures, protein families absent from the training set, variations in experimental conditions, and evolving drug resistance mechanisms. These factors can lead to a significant degradation in performance, causing models to suggest suboptimal compounds or overlook promising candidates—challenges commonly referred to as Out-of-Domain (OOD) in the machine learning community. To address the OOD challenges in binding affinity algorithm development, several benchmarks have been introduced. However, we observe that many lack a convenient codebase framework for swift algorithm evaluation.In this paper, building upon the DrugOOD dataset, we introduce a comprehensive benchmarking framework to assess the resilience and adaptability of OOD algorithms in binding affinity prediction. Our framework offers a streamlined approach for evaluating algorithmic performance in OOD scenarios. Furthermore, we propose a method that surpasses existing state-of-the-art approaches in our benchmark tests. We anticipate that our contributions will spur further research addressing OOD challenges and enhance the reliability and robustness of binding affinity predictions in drug design. Code available at: https://github.com/zehanzz/BioFrontierOOD.git

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Section: Domain Generalizationmentioning

confidence: 99%

Toward Out-of-Domain Binding Affinity Prediction

Zhao,

Fang,

Wang

2023

Preprint

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“…It extends the setup of domain adaptation to a setting in which the testing distribution data, even unlabelled, is not available during training. Instead, models are trained with data from multiple training distributions, and enforcing invariance across these training distributions has become a major theme [1,10,19,22,42,47,52,54,69].…”

Section: Domain Adaptation Domain Generalization and New Paradigmsmentioning

confidence: 99%

The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization

Huang¹,

Wang²,

Dong³

et al. 2022

Preprint

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Training with an emphasis on "hard-to-learn" components of the data has been proven as an effective method to improve the generalization of machine learning models, especially in the settings where robustness (e.g., generalization across distributions) is valued. Existing literature discussing this "hard-to-learn" concept are mainly expanded either along the dimension of the samples or the dimension of the features. In this paper, we aim to introduce a simple view merging these two dimensions, leading to a new, simple yet effective, heuristic to train machine learning models by emphasizing the worst-cases on both the sample and the feature dimensions. We name our method W2D following the concept of "Worst-case along Two Dimensions". We validate the idea and demonstrate its empirical strength over standard benchmarks.

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Learning Domain Invariant Representations in Goal-conditioned Block MDPs

Cited by 2 publications

References 14 publications

Toward Out-of-Domain Binding Affinity Prediction

Toward Out-of-Domain Binding Affinity Prediction

The Two Dimensions of Worst-case Training and the Integrated Effect for Out-of-domain Generalization

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