Cover Image

Distance metric learning for conditional anomaly detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Valko, Michal, Hauskrecht, Milos
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Feature importance analysis for patient management decisions
by: Valko, Michal, et al.
Published: (2026)

Evidence-based anomaly detection in clinical domains
by: Hauskrecht, Milos, et al.
Published: (2026)

Conditional anomaly detection with soft harmonic functions
by: Valko, Michal, et al.
Published: (2026)

Conditional anomaly detection using soft harmonic functions: An application to clinical alerting
by: Valko, Michal, et al.
Published: (2026)

Conditional anomaly detection methods for patient-management alert systems
by: Valko, Michal, et al.
Published: (2026)

Adaptive graph-based algorithms for conditional anomaly detection and semi-supervised learning
by: Valko, Michal
Published: (2026)

Learning predictive models for combinations of heterogeneous proteomic data sources
by: Valko, Michal, et al.
Published: (2026)

Conditional outlier detection for clinical alerting
by: Hauskrecht, Milos, et al.
Published: (2026)

Outlier detection for patient monitoring and alerting
by: Hauskrecht, Miloš, et al.
Published: (2026)

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction
by: Joshi, Ankitkumar, et al.
Published: (2025)

Middle-mile logistics through the lens of goal-conditioned reinforcement learning
by: Eberhard, Onno, et al.
Published: (2026)

Bandits on graphs and structures
by: Valko, Michal
Published: (2026)

Online learning with noisy side observations
by: Kocák, Tomáš, et al.
Published: (2026)

Online learning with Erdős-Rényi side-observation graphs
by: Kocák, Tomáš, et al.
Published: (2026)

Adaptive multi-fidelity optimization with fast learning rates
by: Fiegel, Come, et al.
Published: (2026)

Large-scale semi-supervised learning with online spectral graph sparsification
by: Calandriello, Daniele, et al.
Published: (2026)

Pack only the essentials: Adaptive dictionary learning for kernel ridge regression
by: Calandriello, Daniele, et al.
Published: (2026)

VA-learning as a more efficient alternative to Q-learning
by: Tang, Yunhao, et al.
Published: (2023)

Online combinatorial optimization with stochastic decision sets and adversarial losses
by: Neu, Gergely, et al.
Published: (2026)

Learning from a single labeled face and a stream of unlabeled data
by: Kveton, Branislav, et al.
Published: (2026)

Revealing graph bandits for maximizing local influence
by: Carpentier, Alexandra, et al.
Published: (2026)

Extreme bandits
by: Carpentier, Alexandra, et al.
Published: (2026)

Semi-supervised learning with max-margin graph cuts
by: Kveton, Branislav, et al.
Published: (2026)

Efficient learning by implicit exploration in bandit problems with side observations
by: Kocak, Tomas, et al.
Published: (2026)

Improved large-scale graph learning through ridge spectral sparsification
by: Calandriello, Daniele, et al.
Published: (2026)

Online semi-supervised perception: Real-time learning without explicit feedback
by: Kveton, Branislav, et al.
Published: (2026)

Active multiple matrix completion with adaptive confidence sets
by: Locatelli, Andrea, et al.
Published: (2026)

Bandits attack function optimization
by: Preux, Philippe, et al.
Published: (2026)

Stochastic simultaneous optimistic optimization
by: Valko, Michal, et al.
Published: (2026)

Analysis of Nystrom method with sequential ridge leverage scores
by: Calandriello, Daniele, et al.
Published: (2026)

Bayesian policy gradient and actor-critic algorithms
by: Ghavamzadeh, Mohammad, et al.
Published: (2026)

Covariance-adapting algorithm for semi-bandits with application to sparse rewards
by: Perrault, Pierre, et al.
Published: (2026)

Language Generation with Replay: A Learning-Theoretic View of Model Collapse
by: Racca, Giorgio, et al.
Published: (2026)

Towards eco friendly cybersecurity: machine learning based anomaly detection with carbon and energy metrics
by: Aashish, KC, et al.
Published: (2025)

Black-box optimization of noisy functions with unknown smoothness
by: Grill, Jean-Bastien, et al.
Published: (2026)

Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
by: Grill, Jean-Bastien, et al.
Published: (2026)

Understanding the limitations of self-supervised learning for tabular anomaly detection
by: Mai, Kimberly T., et al.
Published: (2023)

On two ways to use determinantal point processes for Monte Carlo integration
by: Gautier, Guillaume, et al.
Published: (2026)

Monotonic anomaly detection
by: Lenz, Oliver Urs, et al.
Published: (2024)

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning
by: Audiffren, Julien, et al.
Published: (2026)