arXiv Machine Learning
Jun 8
Uncertainty-Guided Label Rebalancing for CPS Safety Monitoring
arXiv:2603. 25670v3 Announce Type: replace Abstract: Safety monitoring is essential for Cyber-Physical Systems (CPSs).
PB-OEL: A Performance-Bounded Online Ensemble Learning Framework With Mixed Feedback for Real-Time Safety Assessment
arXiv:2503. 15581v2 Announce Type: replace Abstract: Real-time safety assessment is critical for ensuring the reliable operation of complex dynamic systems.
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arXiv Machine Learning
Aug 14
Defensive Boosting for Online Probabilistic Forecasting
arXiv:2608. 13554v1 Announce Type: new Abstract: We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary.
arXiv Machine Learning
Aug 12
Optimized Sequential Testing for Binary Ensemble Classifiers
arXiv:2606. 15237v1 Announce Type: cross Abstract: Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote.
arXiv AI
Sep 1
Towards Stream Learning on Embedded Systems: Benchmarking the Memory Consumption of Stream Learning Methods
arXiv:2608.30923v1 Announce Type: cross Abstract: Stream learning is commonly evaluated through predictive performance and adaptation to concept drift. However, sustained operation of a stream learne...
arXiv Machine Learning
Jul 27
A Leakage-Free Stacked Ensemble Method for Multiclass Classification
arXiv:2607. 22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains.
arXiv AI
Aug 17
Regime-Conditional Verification: Correctness Estimation for Adapting and Monitoring Safety Classifiers
arXiv:2608. 14089v1 Announce Type: new Abstract: Safety classifiers deployed with large language models often fail for two reasons: their decisions reflect the policy learned during training rather than the deployer's desired policy, and their performance degrades as deployment traffic evolves.
arXiv AI
Aug 13
CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification
arXiv:2608. 11287v1 Announce Type: cross Abstract: Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepresented classes.
arXiv Machine Learning
Sep 18
Online Adaptive Kernel Mixing for Gaussian Process Decision Making
The paper introduces HACK GPs, a method that treats kernel selection for Gaussian Processes as an online learning problem with expert advice. Each candidate kernel is viewed as a GP expert, and a distribution over these experts is updated online using AdaHedge based on a loss that reflects both function fit and task alignment. Two variants—Mixture of Gaussians and categorical sampling—are presented, with theoretical guarantees that the weight concentrates on the best kernel under a loss‑gap condition, and empirical results show robust performance across Bayesian optimization, level set estimation, and Bayesian active learning compared to standard kernels and simple ensembles.
arXiv Machine Learning
Aug 10
Online Conformal Prediction Beyond Feedback
arXiv:2608. 07139v1 Announce Type: new Abstract: Uncertainty quantification is essential when deploying machine learning models in safety-critical applications.
arXiv Machine Learning
Aug 24
Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades
arXiv:2604.14251v2 Announce Type: replace Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be es...
arXiv Machine Learning
Jun 4
Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimation
arXiv:2602. 08142v2 Announce Type: replace Abstract: Machine learning applications require fast and reliable per-sample uncertainty estimation.
arXiv Machine Learning
Jun 2
Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA
arXiv:2606. 00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems.