Not all datasets are born equal: On heterogeneous tabular data and adversarial examples

Mathov, Yael; Levy, Eden; Katzir, Ziv; Shabtai, Asaf; Elovici, Yuval

doi:10.1016/j.knosys.2022.108377

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…However, it is constrained by its linearity and dependence on well‐defined hypothesis functions. In contrast, logistic regression handles classification tasks by constraining outputs between 0 and 1, making it versatile for multi‐class scenarios 46 . Naive Bayes, akin to logistic regression, predicts class labels based on joint probability calculations using Bayes Theorem, assuming feature independence, though this assumption may limit its effectiveness in complex or correlated datasets.…”

Section: Integration Of Ai Workflow With Organoid Systemsmentioning

confidence: 99%

“…In contrast, logistic regression handles classification tasks by constraining outputs between 0 and 1, making it versatile for multi-class scenarios. 46 Naive Bayes, akin to logistic regression, predicts class labels based on joint probability calculations using Bayes Theorem, assuming feature independence, though this assumption may limit its effectiveness in complex or correlated datasets. Support-Vector Machines (SVM) aim to maximize class separation by identifying optimal hyperplanes, with the capacity to employ non-linear kernels, but they may overfit and demand significant computation, limiting suitability for large, intricate datasets.…”

Section: Supervised Machine Learning Modelsmentioning

confidence: 99%

See 1 more Smart Citation

AI‐organoid integrated systems for biomedical studies and applications

Maramraju,

Kowalczewski,

Kaza

et al. 2024

Bioengineering & Transla Med

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In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)‐derived organoids. Stem cell‐derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error‐prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid‐based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label‐free organoid recognition, and three‐dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI‐organoid integration, focusing on the establishment of reliable AI model decision‐making processes and the standardization of organoid research.

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Section: Integration Of Ai Workflow With Organoid Systemsmentioning

confidence: 99%

Section: Supervised Machine Learning Modelsmentioning

confidence: 99%

AI‐organoid integrated systems for biomedical studies and applications

Maramraju,

Kowalczewski,

Kaza

et al. 2024

Bioengineering & Transla Med

View full text Add to dashboard Cite

show abstract

“…Tree-based models have long been a favourite for making decisions in high-stakes domains such as medicine and finance, due to their interpretability and exceptional performance on structured data [1]. However, recent results have highlighted that tree-based models are, similarly to other machine learning models [2,3], also highly susceptible to adversarial examples [4][5][6], raising concerns about their use in high-stakes domains where errors can have dire consequences.…”

Section: Introductionmentioning

confidence: 99%

(De-)Randomized Smoothing for Decision Stump Ensembles

Horváth¹,

Niklas²,

Fischer³

et al. 2022

Preprint

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Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored, in contrast to those focusing on neural networks. Targeting this important challenge, we propose deterministic smoothing for decision stump ensembles. Whereas most prior work on randomized smoothing focuses on evaluating arbitrary base models approximately under input randomization, the key insight of our work is that decision stump ensembles enable exact yet efficient evaluation via dynamic programming. Importantly, we obtain deterministic robustness certificates, even jointly over numerical and categorical features, a setting ubiquitous in the real world. Further, we derive an MLE-optimal training method for smoothed decision stumps under randomization and propose two boosting approaches to improve their provable robustness. An extensive experimental evaluation shows that our approach yields significantly higher certified accuracies than the state-of-the-art for tree-based models. We release all code and trained models at ANONYMIZED. * Equal contribution Preprint. Under review.

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“…Also, counterfactual approaches are not suited to understand why entities are classified as being part of a rumour, due to their focus on features that may change the result [9]. As such, explanations shall be based on a set of related examples, which enable users to generalize their properties [10]. For a rumour represented as a graph, the explanation is then given in terms of related subgraphs of rumours detected in the past.…”

Section: Introductionmentioning

confidence: 99%

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

Nguyên¹,

Phan²,

Nguyen³

et al. 2022

Preprint

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The propagation of rumours on social media poses an important threat to societies, so that various techniques for rumour detection have been proposed recently. Yet, existing work focuses on what entities constitute a rumour, but provides little support to understand why the entities have been classified as such. This prevents an effective evaluation of the detected rumours as well as the design of countermeasures. In this work, we argue that explanations for detected rumours may be given in terms of examples of related rumours detected in the past. A diverse set of similar rumours helps users to generalize, i.e., to understand the properties that govern the detection of rumours. Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the k most similar and diverse subgraphs from past rumours. The challenge is that all of the computations require fast assessment of similarities between graphs. To achieve an efficient and adaptive realization of the approach in a streaming setting, we present a novel graph representation learning technique and report on implementation considerations. Our evaluation experiments show that our approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.

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Not all datasets are born equal: On heterogeneous tabular data and adversarial examples

Cited by 12 publications

References 13 publications

AI‐organoid integrated systems for biomedical studies and applications

AI‐organoid integrated systems for biomedical studies and applications

(De-)Randomized Smoothing for Decision Stump Ensembles

Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

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