The vast repositories of Electronic Health Records (EHR) and medical claims hold untapped potential for studying rare but critical events, such as suicide attempt. Conventional setups often model suicide attempt as a univariate outcome and also exclude any ``single-record'' patients with a single documented encounter due to a lack of historical information. However, patients who were diagnosed with suicide attempts at the only encounter could, to some surprise, represent a substantial proportion of all attempt cases in the data, as high as 70--80%. We innovate a hybrid and integrative learning framework to leverage concurrent outcomes as surrogates and harness the forbidden yet precious information from single-record data. Our approach employs a supervised learning component to learn the latent variables that connect primary (e.g., suicide) and surrogate outcomes (e.g., mental disorders) to historical information. It simultaneously employs an unsupervised learning component to utilize the single-record data, through the shared latent variables. As such, our approach offers a general strategy for information integration that is crucial to modeling rare conditions and events. With hospital inpatient data from Connecticut, we demonstrate that single-record data and concurrent diagnoses indeed carry valuable information, and utilizing them can substantially improve suicide risk modeling.

Ensuring a stable power supply in rural areas relies heavily on effective inspection of power equipment, particularly transmission lines (TLs). However, detecting TLs from aerial imagery can be challenging when dealing with misalignments between visible light (RGB) and infrared (IR) images, as well as mismatched high- and low-level features in convolutional networks. To address these limitations, we propose a novel Hierarchical Multi-Modal Enhancement Network (HMMEN) that integrates RGB and IR data for robust and accurate TL detection. Our method introduces two key components: (1) a Mutual Multi-Modal Enhanced Block (MMEB), which fuses and enhances hierarchical RGB and IR feature maps in a coarse-to-fine manner, and (2) a Feature Alignment Block (FAB) that corrects misalignments between decoder outputs and IR feature maps by leveraging deformable convolutions. We employ MobileNet-based encoders for both RGB and IR inputs to accommodate edge-computing constraints and reduce computational overhead. Experimental results on diverse weather and lighting conditionsfog, night, snow, and daytimedemonstrate the superiority and robustness of our approach compared to state-of-the-art methods, resulting in fewer false positives, enhanced boundary delineation, and better overall detection performance. This framework thus shows promise for practical large-scale power line inspections with unmanned aerial vehicles.

Large Language Models (LLMs) excel in text generation and understanding, especially in simulating socio-political and economic patterns, serving as an alternative to traditional surveys. However, their global applicability remains questionable due to unexplored biases across socio-demographic and geographic contexts. This study examines how LLMs perform across diverse populations by analyzing public surveys from Chile and the United States, focusing on predictive accuracy and fairness metrics. The results show performance disparities, with LLM consistently outperforming on U.S. datasets. This bias originates from the U.S.-centric training data, remaining evident after accounting for socio-demographic differences. In the U.S., political identity and race significantly influence prediction accuracy, while in Chile, gender, education, and religious affiliation play more pronounced roles. Our study presents a novel framework for measuring socio-demographic biases in LLMs, offering a path toward ensuring fairer and more equitable model performance across diverse socio-cultural contexts.

Model monitoring involves analyzing AI algorithms once they have been deployed and detecting changes in their behaviour. This thesis explores machine learning model monitoring ML before the predictions impact real-world decisions or users. This step is characterized by one particular condition: the absence of labelled data at test time, which makes it challenging, even often impossible, to calculate performance metrics. The thesis is structured around two main themes: (i) AI alignment, measuring if AI models behave in a manner consistent with human values and (ii) performance monitoring, measuring if the models achieve specific accuracy goals or desires. The thesis uses a common methodology that unifies all its sections. It explores feature attribution distributions for both monitoring dimensions. Using these feature attribution explanations, we can exploit their theoretical properties to derive and establish certain guarantees and insights into model monitoring.

Principal component analysis is commonly used for dimensionality reduction, feature extraction, denoising, and visualization. The most commonly used principal component analysis method is based upon optimization of the L2-norm, however, the L2-norm is known to exaggerate the contribution of errors and outliers. When optimizing over the L1-norm, the components generated are known to exhibit robustness or resistance to outliers in the data. The L1-norm components can be solved for with a binary optimization problem. Previously, L1-BF has been used to solve the binary optimization for multiple components simultaneously. In this paper we propose QAPCA, a new method for finding principal components using quantum annealing hardware which will optimize over the robust L1-norm. The conditions required for convergence of the annealing problem are discussed. The potential speedup when using quantum annealing is demonstrated through complexity analysis and experimental results. To showcase performance against classical principal component analysis techniques experiments upon synthetic Gaussian data, a fault detection scenario and breast cancer diagnostic data are studied. We find that the reconstruction error when using QAPCA is comparable to that when using L1-BF.

The increasing sophistication of encryption-based ransomware has demanded innovative approaches to detection and mitigation, prompting the development of a hierarchical framework grounded in probabilistic cryptographic analysis. By focusing on the statistical characteristics of encryption patterns, the proposed methodology introduces a layered approach that combines advanced clustering algorithms with machine learning to isolate ransomware-induced anomalies. Through comprehensive testing across diverse ransomware families, the framework demonstrated exceptional accuracy, effectively distinguishing malicious encryption operations from benign activities while maintaining low false positive rates. The system's design integrates dynamic feedback mechanisms, enabling adaptability to varying cryptographic complexities and operational environments. Detailed entropy-based evaluations revealed its sensitivity to subtle deviations in encryption workflows, offering a robust alternative to traditional detection methods reliant on static signatures or heuristics. Computational benchmarks confirmed its scalability and efficiency, achieving consistent performance even under high data loads and complex cryptographic scenarios. The inclusion of real-time clustering and anomaly evaluation ensures rapid response capabilities, addressing critical latency challenges in ransomware detection. Performance comparisons with established methods highlighted its improvements in detection efficacy, particularly against advanced ransomware employing extended key lengths and unique cryptographic protocols.

One contribution seems to have been in defining a surrogate functional g (line 166) that replaces the \mu(0) term in a denominator term with an arbitrary parameter c and then using a uniform convergence bound over values of c to ensure that estimation does take place even if c is replaced with its actual value of \mu(0). Another contribution seems to be in fine tuning the proof technique used to prove Proposition 5. The main contribution is a proof for obtaining generalization bound for weighted areas under the ROC curve for Lipschitz weight functions.

This is a theoretical paper that has received relatively good reviews. However, two of the reviewers only increased their scores from 5 to 6 in order to reduce the divergence and help form a consensus (in the discussions), but neither was really convinced about the quality of the paper. Unfortunately, the highest scoring reviewer was also the least confident. I read the paper myself and I find that it has some merits --- it seems theoretically solid, but I have a slight tendency towards saying that it may be a better fit at ALT/AISTATS/COLT, and it is unclear if the NeurIPS community will benefit from knowing these results. Nevertheless, regardless of the final outcome, the authors are encouraged to improve the readability of their paper through (it is currently somewhat dense for the average reader).

Weaknesses: Given the paper's current state, I have following major comments: - The proposed method's motivation is to tackle the issue of model-data mismatch by modeling the context representation uncertainty. However the notion of the model-data mismatch is loosely defined. It would be more interesting if the paper's formulation would fomulate this problem in a principled way, e.g. the model-data mismatch problem can be framed in a more principled way, e.g. The combined objective of two models with/without bootstraps is somewhat questionable. The computation of residuals would influence a lot to the input hence the convergence of the full model.

This is an important paper on uncertainty quantification. However as the reviewers noted the main concerns are competitiveness with reespect to GPs and also an analysis (perrhaps with intuitions) of when the method underperforms would be useful. Overall, this paper might pave the way for really interesting follow-ups which will build on top of it.