Papers Outlier Detection
“Outlier Detection” 태그가 달린 논문 818편 · 필터 해제
Efficient Estimation of High Information Projections using Nearest Neighbours
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interesting projections of multivariate data. Following similar intuitive motivation to a number of existing techniques,…
Dimensionality ReductionOutlier DetectionImproving Energy Efficiency of Oil Platforms Through Optimal Loading of Diesel Generators Using Machine Learning and Search Algorithms
Rising energy demand, fossil fuel depletion and climate change highlight the need for more efficient energy production and consumption. Offshore oil and gas platforms face challenges related to inefficient energy use, sy…
Outlier DetectionVideoRun2D Demo: Markerless Body Tracking for Biomechanical Analysis of Running
Human pose estimation has advanced significantly due to the development of deep learning models, increased data availability, and improved computing resources. These developments have led to highly accurate body tracking…
Outlier DetectionPose EstimationWasserstein Filtering: A Sample Selection Method for Robust Distribution Learning
Given a dataset where a portion of the samples are contaminated, our goal is to recover the underlying clean population distribution. To this end, we propose Wasserstein Filtering (WF), a novel sample selection framework…
Outlier DetectionAnomaly DetectionGeneralised Robust Bayes for Joint Inference of Model and Contamination
Generalised Bayesian inference (GBI) has emerged as a compelling robust alternative to standard Bayesian inference, mitigating sensitivity to data contamination by replacing the log-likelihood with a robust loss or diver…
Bayesian InferenceOutlier DetectionContaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination and heterogeneity make robust borrowing challenging. We study a contam…
Multi-Task LearningFederated LearningOutlier DetectionWhat Drives the Inlier-Memorization Effect? A Theory of Outlier Detection via Early Training Dynamics
Outlier detection (OD) aims to identify anomalous instances by learning the underlying structure of normal data (inliers), and is particularly challenging in fully unsupervised settings where no information about anomali…
Outlier DetectionExplainable Outlier Detection for Interval-valued Data
Explainability is increasingly recognized as a key aspect of outlier detection. However, for complex data structures such as interval-valued data, it remains largely unexplored. Building on an outlier detection framework…
Outlier DetectionReliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation
Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, where corrupted inputs also destabilize online updates. We study robust te…
Test-time AdaptationOutlier DetectionARMOR-MAD: Adaptive Routing for Heterogeneous Multi-Agent Debate in Large Language Model Reasoning
Multi-agent debate (MAD) can improve large language model reasoning, but fixed debate pipelines often waste computation and can amplify correlated errors among similar agents. We propose ARMOR-MAD, a training-free hetero…
Outlier DetectionGraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a…
Node ClassificationCommunity DetectionOutlier DetectionCordyceps: Covert Control Attacks on LLMs via Data Poisoning
Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on fixed trigger phrases that defenses such as outlier detection, clean-…
Outlier DetectionEverywhere Valid Bounds on False Discovery Proportions in Conformal Inference
Modern applications of conformal inference to multiple testing problems, such as outlier detection and candidate selection, often involve selecting test samples whose conformal p-values fall below a threshold. The qualit…
Outlier DetectionAutomatic Unsupervised Ensemble Outlier Model Selection--Extended Version
Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve detection robustness. However, composing an ensemble without labeled data …
Outlier DetectionOutlier detection for patient monitoring and alerting
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electronic health records (EHRs). Our hypothesis is that a patient-manageme…
Outlier DetectionSimple KNN-Based Outlier Detection Achieves Robust Clustering
Being robust to the presence of outliers is crucial for applying clustering algorithms in practice. In the $\textit{robust $k$-Means}$ problem (i.e., $k$-Means with outliers), the goal is to remove $z$ outliers and minim…
Outlier DetectionConditional outlier detection for clinical alerting
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-mana…
Outlier DetectionUnsupervised Machine Learning for Detecting Structural Anomalies in European Regional Statistics
Ensuring the coherence of regional socio-economic statistics is a central task for national statistical institutes. Traditional validation tools, such as range edits, ratio checks, or univariate outlier detection, are ef…
Outlier DetectionAnomaly DetectionSample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
Federated learning is a machine learning paradigm in which multiple devices collaboratively train a model under the supervision of a central server while ensuring data privacy. However, its performance is often hindered …
Image ClassificationFederated LearningOutlier DetectionUncovering Latent Patterns in Social Media Usage and Mental Health: A Clustering-Based Approach Using Unsupervised Machine Learning
The widespread adoption of social media has heightened interest in its psychological effects, particularly on mental health indicators such as anxiety, depression, loneliness, and sleep quality, as these platforms increa…
Outlier DetectionSleep Quality