Sample size calculation is an essential step in most data-based disciplines. Large enough samples ensure representativeness of the population and determine the precision of estimates. This is true for most quantitative studies, including those that employ machine learning methods, such as natural language processing, where free-text is used to generate predictions and classify instances of text. Within the healthcare domain, the lack of sufficient corpora of previously collected data can be a limiting factor when determining sample sizes for new studies. This paper tries to address the issue by making recommendations on sample sizes for text classification tasks in the healthcare domain. Models trained on the MIMIC-III database of critical care records from Beth Israel Deaconess Medical Center were used to classify documents as having or not having Unspecified Essential Hypertension, the most common diagnosis code in the database. Simulations were performed using various classifiers on different sample sizes and class proportions. This was repeated for a comparatively less common diagnosis code within the database of diabetes mellitus without mention of complication. Smaller sample sizes resulted in better results when using a K-nearest neighbours classifier, whereas larger sample sizes provided better results with support vector machines and BERT models. Overall, a sample size larger than 1000 was sufficient to provide decent performance metrics. The simulations conducted within this study provide guidelines that can be used as recommendations for selecting appropriate sample sizes and class proportions, and for predicting expected performance, when building classifiers for textual healthcare data. The methodology used here can be modified for sample size estimates calculations with other datasets.

Noise is a pervasive element within real-world measurement data, significantly undermining the performance of network inference models. However, the quest for a comprehensive enhancement framework capable of bolstering noise resistance across a diverse array of network inference models has remained elusive. Here, we present an elegant and efficient framework tailored to amplify the capabilities of network inference models in the presence of noise. Leveraging curriculum learning, we mitigate the deleterious impact of noisy samples on network inference models. Our proposed framework is model-agnostic, seamlessly integrable into a plethora of model-based and model-free network inference methods. Notably, we utilize one model-based and three model-free network inference methods as the foundation. Extensive experimentation across various synthetic and real-world networks, encapsulating diverse nonlinear dynamic processes, showcases substantial performance augmentation under varied noise types, particularly thriving in scenarios enriched with clean samples. This framework's adeptness in fortifying both model-free and model-based network inference methodologies paves the avenue towards a comprehensive and unified enhancement framework, encompassing the entire spectrum of network inference models. Available Code: https://github.com/xiaoyuans/MANIE.

Anomaly detection is an important task for complex systems (e.g., industrial facilities, manufacturing, large-scale science experiments), where failures in a sub-system can lead to low yield, faulty products, or even damage to components. While complex systems often have a wealth of data, labeled anomalies are typically rare (or even nonexistent) and expensive to acquire. Unsupervised approaches are therefore common and typically search for anomalies either by distance or density of examples in the input feature space (or some associated low-dimensional representation). This paper presents a novel approach called CoAD, which is specifically designed for multi-modal tasks and identifies anomalies based on \textit{coincident} behavior across two different slices of the feature space. We define an \textit{unsupervised} metric, $\hat{F}_\beta$, out of analogy to the supervised classification $F_\beta$ statistic. CoAD uses $\hat{F}_\beta$ to train an anomaly detection algorithm on \textit{unlabeled data}, based on the expectation that anomalous behavior in one feature slice is coincident with anomalous behavior in the other. The method is illustrated using a synthetic outlier data set and a MNIST-based image data set, and is compared to prior state-of-the-art on two real-world tasks: a metal milling data set and a data set from a particle accelerator.

In this paper, we propose a novel probabilistic self-supervised learning via Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic models to enhance representation quality and mitigate collapsing representations. Our proposed approach involves two neural networks; the online network and the target network, which collaborate and learn the diverse distribution of representations from each other through knowledge distillation. By presenting the input samples in two augmented formats, the online network is trained to predict the target network representation of the same sample under a different augmented view. The two networks are trained via our new loss function based on proper scoring rules. We provide a theoretical justification for ProSMIN's convergence, demonstrating the strict propriety of its modified scoring rule. This insight validates the method's optimization process and contributes to its robustness and effectiveness in improving representation quality. We evaluate our probabilistic model on various downstream tasks, such as in-distribution generalization, out-of-distribution detection, dataset corruption, low-shot learning, and transfer learning. Our method achieves superior accuracy and calibration, surpassing the self-supervised baseline in a wide range of experiments on large-scale datasets like ImageNet-O and ImageNet-C, ProSMIN demonstrates its scalability and real-world applicability.

Assessing the fidelity and diversity of the generative model is a difficult but important issue for technological advancement. So, recent papers have introduced k-Nearest Neighbor ($k$NN) based precision-recall metrics to break down the statistical distance into fidelity and diversity. While they provide an intuitive method, we thoroughly analyze these metrics and identify oversimplified assumptions and undesirable properties of kNN that result in unreliable evaluation, such as susceptibility to outliers and insensitivity to distributional changes. Thus, we propose novel metrics, P-precision and P-recall (PP\&PR), based on a probabilistic approach that address the problems. Through extensive investigations on toy experiments and state-of-the-art generative models, we show that our PP\&PR provide more reliable estimates for comparing fidelity and diversity than the existing metrics. The codes are available at \url{https://github.com/kdst-team/Probablistic_precision_recall}.

Machine learning can provide predictions with disparate outcomes, in which subgroups of the population (e.g., defined by age, gender, or other sensitive attributes) are systematically disadvantaged. In order to comply with upcoming legislation, practitioners need to locate such disparate outcomes. However, previous literature typically detects disparities through statistical procedures for when the sensitive attribute is specified a priori. This limits applicability in real-world settings where datasets are high dimensional and, on top of that, sensitive attributes may be unknown. As a remedy, we propose a data-driven framework called Automatic Location of Disparities (ALD) which aims at locating disparities in machine learning. ALD meets several demands from industry: ALD (1) is applicable to arbitrary machine learning classifiers; (2) operates on different definitions of disparities (e.g., statistical parity or equalized odds); and (3) deals with both categorical and continuous predictors even if disparities arise from complex and multi-way interactions known as intersectionality (e. g., age above 60 and female). ALD produces interpretable audit reports as output. We demonstrate the effectiveness of ALD based on both synthetic and real-world datasets. As a result, we empower practitioners to effectively locate and mitigate disparities in machine learning algorithms, conduct algorithmic audits, and protect individuals from discrimination.

We consider the problem of inferring the underlying graph topology from smooth graph signals in a novel but practical scenario where data are located in distributed clients and are privacy-sensitive. The main difficulty of this task lies in how to utilize the potentially heterogeneous data of all isolated clients under privacy constraints. Towards this end, we propose a framework where personalized graphs for local clients as well as a consensus graph are jointly learned. The personalized graphs match local data distributions, thereby mitigating data heterogeneity, while the consensus graph captures the global information. We next devise a tailored algorithm to solve the induced problem without violating privacy constraints, i.e., all private data are processed locally. To further enhance privacy protection, we introduce differential privacy (DP) into the proposed algorithm to resist privacy attacks when transmitting model updates. Theoretically, we establish provable convergence analyses for the proposed algorithms, including that with DP. Finally, extensive experiments on both synthetic and real-world data are carried out to validate the proposed framework. Experimental results illustrate that our approach can learn graphs effectively in the target scenario.

Background and Objective: Colorectal cancer is a high mortality cancer. Clinical data analysis plays a crucial role in predicting the survival of colorectal cancer patients, enabling clinicians to make informed treatment decisions. However, utilizing clinical data can be challenging, especially when dealing with imbalanced outcomes. This paper focuses on developing algorithms to predict 1-, 3-, and 5-year survival of colorectal cancer patients using clinical datasets, with particular emphasis on the highly imbalanced 1-year survival prediction task. To address this issue, we propose a method that creates a pipeline of some of standard balancing techniques to increase the true positive rate. Evaluation is conducted on a colorectal cancer dataset from the SEER database. Methods: The pre-processing step consists of removing records with missing values and merging categories. The minority class of 1-year and 3-year survival tasks consists of 10% and 20% of the data, respectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN), Synthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and RENN approaches were used and compared for balancing the data with tree-based classifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient Boosting, and Light Gradient Boosting (LGBM) are used in this article. Method. Results: The performance evaluation utilizes a 5-fold cross-validation approach. In the case of highly imbalanced datasets (1-year), our proposed method with LGBM outperforms other sampling methods with the sensitivity of 72.30%. For the task of imbalance (3-year survival), the combination of RENN and LGBM achieves a sensitivity of 80.81%, indicating that our proposed method works best for highly imbalanced datasets. Conclusions: Our proposed method significantly improves mortality prediction for the minority class of colorectal cancer patients.

Room geometry is important prior information for implementing realistic 3D audio rendering. For this reason, various room geometry inference (RGI) methods have been developed by utilizing the time of arrival (TOA) or time difference of arrival (TDOA) information in room impulse responses. However, the conventional RGI technique poses several assumptions, such as convex room shapes, the number of walls known in priori, and the visibility of first-order reflections. In this work, we introduce the deep neural network (DNN), RGI-Net, which can estimate room geometries without the aforementioned assumptions. RGI-Net learns and exploits complex relationships between high-order reflections in room impulse responses (RIRs) and, thus, can estimate room shapes even when the shape is non-convex or first-order reflections are missing in the RIRs. The network takes RIRs measured from a compact audio device equipped with a circular microphone array and a single loudspeaker, which greatly improves its practical applicability. RGI-Net includes the evaluation network that separately evaluates the presence probability of walls, so the geometry inference is possible without prior knowledge of the number of walls.

The sharing of microdata, such as fund holdings and derivative instruments, by regulatory institutions presents a unique challenge due to strict data confidentiality and privacy regulations. These challenges often hinder the ability of both academics and practitioners to conduct collaborative research effectively. The emergence of generative models, particularly diffusion models, capable of synthesizing data mimicking the underlying distributions of real-world data presents a compelling solution. This work introduces 'FinDiff', a diffusion model designed to generate real-world financial tabular data for a variety of regulatory downstream tasks, for example economic scenario modeling, stress tests, and fraud detection. The model uses embedding encodings to model mixed modality financial data, comprising both categorical and numeric attributes. The performance of FinDiff in generating synthetic tabular financial data is evaluated against state-of-the-art baseline models using three real-world financial datasets (including two publicly available datasets and one proprietary dataset). Empirical results demonstrate that FinDiff excels in generating synthetic tabular financial data with high fidelity, privacy, and utility.