Georgios I Liapis,Sophia Tsoka,Lazaros G Papageorgiou
Georgios I Liapis
Data classification is considered a fundamental research subject within the machine learning community. Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. Interpretable algorithm...
Eric F Lock
Eric F Lock
Data for several applications in diverse fields can be represented as multiple matrices that are linked across rows or columns. This is particularly common in molecular biomedical research, in which multiple molecular "omics" technologies m...
Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling [0.03%]
可否进行个性化？使用重采样评估在线强化学习算法中的个性化因素
Susobhan Ghosh,Raphael Kim,Prasidh Chhabria et al.
Susobhan Ghosh et al.
There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about whe...
XAI-TRIS: non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance [0.03%]
基于非线性图像的基准数据集以量化特征重要性的假阳性事后归因
Benedict Clark,Rick Wilming,Stefan Haufe
Benedict Clark
The field of 'explainable' artificial intelligence (XAI) has produced highly acclaimed methods that seek to make the decisions of complex machine learning (ML) methods 'understandable' to humans, for example by attributing 'importance' scor...
Deep reinforcement learning for multi-class imbalanced training: applications in healthcare [0.03%]
用于多分类不平衡训练的深度强化学习：在医疗保健领域的应用
Jenny Yang,Rasheed El-Bouri,Odhran O&#x;Donoghue et al.
Jenny Yang et al.
With the rapid growth of memory and computing power, datasets are becoming increasingly complex and imbalanced. This is especially severe in the context of clinical data, where there may be one rare event for many cases in the majority clas...
Learning biologically-interpretable latent representations for gene expression data: Pathway Activity Score Learning Algorithm [0.03%]
具有生物学解释能力的基因表达数据潜在表示学习：通路活性得分学习算法
Ioulia Karagiannaki,Krystallia Gourlia,Vincenzo Lagani et al.
Ioulia Karagiannaki et al.
Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (i.e., high dimensional data). However, lower-dimensional representations that retain the useful biological information do exist. We present...
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics [0.03%]
带有安全约束的贝叶斯优化：机器人中安全且自动的参数调节
Felix Berkenkamp,Andreas Krause,Angela P Schoellig
Felix Berkenkamp
Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been us...
David Schnörr,Christoph Schnörr
David Schnörr
The Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction-diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a ...
Sofie Goethals,David Martens,Toon Calders
Sofie Goethals
This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is a threat to fairness as these models can amplify bias present in the dataset, and there is...
Manuel Schürch,Dario Azzimonti,Alessio Benavoli et al.
Manuel Schürch et al.
Gaussian processes (GPs) are an important tool in machine learning and statistics. However, off-the-shelf GP inference procedures are limited to datasets with several thousand data points because of their cubic computational complexity. For...