Extremile regression, as a least squares analog of quantile regression, is potentially useful tool for modeling and understanding the extreme tails of a distribution. However, existing extremile regression methods, as nonparametric approaches, may face challenges in high-dimensional settings due to data sparsity, computational inefficiency, and the risk of overfitting. While linear regression serves as the foundation for many other statistical and machine learning models due to its simplicity, interpretability, and relatively easy implementation, particularly in high-dimensional settings, this paper introduces a novel definition of linear extremile regression along with an accompanying estimation methodology. The regression coefficient estimators of this method achieve $\sqrt{n}$-consistency, which nonparametric extremile regression may not provide. In particular, while semi-supervised learning can leverage unlabeled data to make more accurate predictions and avoid overfitting to small labeled datasets in high-dimensional spaces, we propose a semi-supervised learning approach to enhance estimation efficiency, even when the specified linear extremile regression model may be misspecified. Both simulation studies and real data analyses demonstrate the finite-sample performance of our proposed methods.

Modern warehouse automation systems rely on fleets of intelligent robots that generate vast amounts of data -- most of which remains unannotated. This paper develops a self-supervised domain adaptation pipeline that leverages real-world, unlabeled data to improve perception models without requiring manual annotations. Our work focuses specifically on estimating the pose and shape of boxes and presents a correct-and-certify pipeline for self-supervised box pose and shape estimation. We extensively evaluate our approach across a range of simulated and real industrial settings, including adaptation to a large-scale real-world dataset of 50,000 images. The self-supervised model significantly outperforms models trained solely in simulation and shows substantial improvements over a zero-shot 3D bounding box estimation baseline. Keywords: Certifiable models, computer vision, 3D robot vision, object pose estimation, safe perception, self-supervised learning.

One of the most difficult challenges in cybersecurity is eliminating Distributed Denial of Service (DDoS) attacks. Automating this task using artificial intelligence is a complex process due to the inherent class imbalance and lack of sufficient labeled samples of real-world datasets. This research investigates the use of Semi-Supervised Learning (SSL) techniques to improve DDoS attack detection when data is imbalanced and partially labeled. In this process, 13 state-of-the-art SSL algorithms are evaluated for detecting DDoS attacks in several scenarios. We evaluate their practical efficacy and shortcomings, including the extent to which they work in extreme environments. The results will offer insight into designing intelligent Intrusion Detection Systems (IDSs) that are robust against class imbalance and handle partially labeled data.

--Reconfigurable intelligent surface (RIS) is regarded as one of the pivotal technologies for sixth-generation wireless communication systems. This paper investigates the downlink transmission of an RIS-assisted multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) communication systems. T o achieve a high system sum rate with low computational complexity, we develop a two-stage unsupervised learning based approach with customized loss function for the RIS reflection phase shift design, active beamforming at base station (BS) and time-frequency resource block (RB) allocation. The proposed approach consists of two neural networks: BeamNet, which takes channel state information (CSI) as input to predict the RIS reflection phase shift, and AllocationNet, which generates RB allocation decisions based on the equivalent CSI from the BS to the users, where the equivalent CSI is obtained by combining the original CSI with the RIS reflection phase shifts predicted by BeamNet. The active beamforming is implemented using the maximum ratio transmission and water-filling algorithm. In order to incorporate the discrete constraints of RIS reflection phase shift and RB allocation decisions into the network while maintaining network differentiability, we introduce a quantization function and the Gumbel softmax trick into BeamNet and AllocationNet, respectively. Furthermore, a customized loss function and phased training strategy are devised to enhance training efficiency and address quality-of-service constraints. Simulation results demonstrate that the proposed approach achieves 99.93% of the system sum rate of the successive convex approximation (SCA) method while requiring only 0.036% of its runtime. Additionally, the method's effectiveness and robustness are validated under different delay tap numbers, user distributions, and Rician factors, demonstrating its strong adaptability to different communication environments. OW ADA YS, with the large-scale deployment of fifth-generation wireless communication systems (5G), the focus of research has gradually shifted to sixth-generation wireless communication systems (6G). Y u Ma, Xingyu Zhou, Xiao Li, and Shi Jin are with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China (e-mail: yuma@seu.edu.cn;

Automating table extraction (TE) from business documents is critical for industrial workflows but remains challenging due to sparse annotations and error-prone multi-stage pipelines. While semi-supervised learning (SSL) can leverage unlabeled data, existing methods rely on confidence scores that poorly reflect extraction quality. We propose QUEST, a Quality-aware Semi-supervised Table extraction framework designed for business documents. QUEST introduces a novel quality assessment model that evaluates structural and contextual features of extracted tables, trained to predict F1 scores instead of relying on confidence metrics. This quality-aware approach guides pseudo-label selection during iterative SSL training, while diversity measures (DPP, Vendi score, IntDiv) mitigate confirmation bias. Experiments on a proprietary business dataset (1000 annotated + 10000 unannotated documents) show QUEST improves F1 from 64% to 74% and reduces empty predictions by 45% (from 12% to 6.5%). On the DocILE benchmark (600 annotated + 20000 unannotated documents), QUEST achieves a 50% F1 score (up from 42%) and reduces empty predictions by 19% (from 27% to 22%). The framework's interpretable quality assessments and robustness to annotation scarcity make it particularly suited for business documents, where structural consistency and data completeness are paramount.

Large network logs, recording multivariate time series generated from heterogeneous devices and sensors in a network, can often reveal important information about abnormal activities, such as network intrusions and device malfunctions. Existing machine learning methods for anomaly detection on multivariate time series typically assume that 1) normal sequences would have consistent behavior for training unsupervised models, or 2) require a large set of labeled normal and abnormal sequences for supervised models. However, in practice, normal network activities can demonstrate significantly varying sequence patterns (e.g., before and after rerouting partial network traffic). Also, the recorded abnormal events can be sparse. This paper presents a novel semi-supervised method that efficiently captures dependencies between network time series and across time points to generate meaningful representations of network activities for predicting abnormal events. The method can use the limited labeled data to explicitly learn separable embedding space for normal and abnormal samples and effectively leverage unlabeled data to handle training data scarcity. The experiments demonstrate that our approach significantly outperformed state-of-the-art approaches for event detection on a large real-world network log.

A key ingredient in machine learning and statistical pipelines alike is the availability of large amounts of high-quality labeled data. Breakthroughs in computer vision stem from the collection of millions of labeled images [8]; social science research relies on extensively labeled datasets to understand human behavior and opinions [22]. While acquiring unlabeled data (e.g., raw images or texts from the internet) can be relatively inexpensive, acquiring high-quality labels is typically an endeavor that requires significant time and effort from human experts. Given the expense of collecting high-quality labels, an enticing prospect is to use increasingly powerful AI models to predict labels for datasets, bypassing the need for human experts entirely. Indeed, recent works have demonstrated AI models' ability to predict protein structures [17], to evaluate language model responses [39], and even to simulate human experimental subjects [23]. These advances highlight the potential for AI to streamline data annotation, and to produce high-quality labels at a fraction of the cost of human experts. The problem with such an approach is that AI models are not always correct, and in particular come with no guarantees on how well they will label a given dataset. This makes it untenable to use AI-predicted labels as a direct substitute for human labels, particularly in settings where label quality is critical--for instance, in high-stakes applications like medical diagnosis, or when the downstream task is to draw conclusions that inform policy decisions. Motivated by this state of affairs, in this paper we ask: Can we leverage powerful AI models to label data, while still guaranteeing quality?

We present a semi-supervised approach that dis-aggregates refugee statistics from administrative boundaries to 0.5-degree grid cells across 25 Sub-Saharan African countries. By integrating UN-HCR's ProGres registration data with satellite-derived building footprints from Google Open Buildings and location coordinates from Open-StreetMap Populated Places, our label spreading algorithm creates spatially explicit refugee statistics at high granularity. This methodology achieves 92.9% average accuracy in placing over 10 million refugee observations into appropriate grid cells, enabling the identification of localized displacement patterns previously obscured in broader regional and national statistics. The resulting high-resolution dataset provides a foundation for a deeper understanding of displacement drivers.

In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scenario in which a company sells a product and can observe only the customers who purchased it. Decision-makers seek to market products effectively based on whether people have loyalty to the company. Individuals with loyalty are those who are likely to remain interested in the company even without additional advertising. Consequently, those loyal customers would likely purchase from the company if they are interested in the product. In contrast, people with lower loyalty may overlook the product or buy similar products from other companies unless they receive marketing attention. Therefore, by focusing marketing efforts on individuals who are interested in the product but do not have strong loyalty, we can achieve more efficient marketing. To achieve this goal, we consider how to learn, from limited data, a classifier that identifies potential customers who (i) have interest in the product and (ii) do not have loyalty to the company. Although our algorithm comprises a single-stage optimization, its objective function implicitly contains two losses derived from standard PU learning settings. For this reason, we refer to our approach as double PU learning. We verify the validity of the proposed algorithm through numerical experiments, confirming that it functions appropriately for the problem at hand.

Semantic segmentation is critical for scene understanding but demands costly pixel-wise annotations, attracting increasing attention to semi-supervised approaches to leverage abundant unlabeled data. While semi-supervised segmentation is often promoted as a path toward scalable, real-world deployment, it is astonishing that current evaluation protocols exclusively focus on segmentation accuracy, entirely overlooking reliability and robustness. These qualities, which ensure consistent performance under diverse conditions (robustness) and well-calibrated model confidences as well as meaningful uncertainties (reliability), are essential for safety-critical applications like autonomous driving, where models must handle unpredictable environments and avoid sudden failures at all costs. To address this gap, we introduce the Reliable Segmentation Score (RSS), a novel metric that combines predictive accuracy, calibration, and uncertainty quality measures via a harmonic mean. RSS penalizes deficiencies in any of its components, providing an easy and intuitive way of holistically judging segmentation models. Comprehensive evaluations of UniMatchV2 against its predecessor and a supervised baseline show that semi-supervised methods often trade reliability for accuracy. While out-of-domain evaluations demonstrate UniMatchV2's robustness, they further expose persistent reliability shortcomings. We advocate for a shift in evaluation protocols toward more holistic metrics like RSS to better align semi-supervised learning research with real-world deployment needs.