Neural nets detect alarming student responses for quick review.
problem Identifying alarming student responses in online assessments.
method Developed neural network models to flag potentially concerning responses.
result Neural nets can flag alarming responses more efficiently than manual review.
Improved RNN predicts patient health from ICU data.
problem Slowed response times and missed true alarms from ICU alarms.
method Compare sliding window and recurrent predictors for ICU multivariate time series.
result RNN slightly improved for three out of four patient state-of-health targets.
Adaptive monitoring for AI systems detects and diagnoses shifts in data distribution.
problem Continuous monitoring of AI systems to detect and address unsafe behavior.
method Weighted-conformal martingales (WCTMs) for online monitoring of AI systems.
result Improved performance over state-of-the-art baselines on real-world datasets.
No-knowledge alarms detect misaligned LLM judges without trusting them.
problem Monitoring LLM judges for consistency when ground truth is unknown.
method Formal logic and Linear Programming to detect disagreements.
result No false positives in detecting misaligned judges.
E-valuator converts verifier scores into reliable decision rules.
problem Ensuring the correctness of agent trajectories based on heuristic scores.
method Sequential hypothesis testing framework for online monitoring of agent trajectories.
result E-valuator provides better false alarm rate control and statistical power than other strategies.
New initialization techniques improve the performance and speed of EMI sensor-based object discrimination.
problem Improving the performance and speed of EMI sensor-based object discrimination.
method Proposed and evaluated new initialization techniques for MI-ACE.
result Comparison of initialization approaches shows improved performance and speed.
One possible way of risk management for an insurance company is to develop an early and appropriate alarm system before the possible ruin. The ruin is defined through the status of the aggregate risk process, which in turn is determined by premium accumulation as well as claim settlement outgo for the insurance company…
Survey of methods to reduce false alarms in IDSs and ADSs.
problem High false alarms in IDSs and ADSs overwhelm administrators.
method Customized anomaly scoring and filtering methods.
result Promising techniques for reducing false alarms exist.
Optimizes latency and false alarm probability in change detection problems.
problem Balancing latency and false alarms in non-stationary environments.
method Develops order-optimal change detectors under specified latency and false alarm levels.
result Derives a universal lower bound on latency and develops order-optimal detectors.
The paper introduces a framework for prescriptive process monitoring that generates alarms to prevent or mitigate undesired outcomes.
problem Existing predictive process monitoring techniques do not prescribe when and how to intervene to decrease undesired outcomes.
method The paper proposes a framework that extends predictive monitoring with the ability to generate alarms, incorporating a cost model to assess the trade-off between generating alarms and the cost of undesired outcomes.
result The net cost of undesired outcomes can be minimized by optimizing the generation of alarms based on the progress of the process instance and introducing delays for triggering alarms.
New models extrapolate false alarms in ASV without new data.
problem Reliable extrapolation of false alarm rates in ASV without new speaker data.
method Generative models in ASV score space for arbitrary systems.
result Models accurately extrapolate false alarm rates for large speaker populations.
Paper tackles ICU false alarms by learning features from ECG signals.
problem High rate of false alarms in ICU due to patient movements and sensor detachment.
method Unsupervised feature learning to extract and cluster high-level features from ECG signals.
result The method reduces false arrhythmia alarms using a few high-level features from a single ECG lead.
Reduces false alarms in ICU monitoring systems.
problem High false alarm rates in ICU monitoring systems.
method Multitask learning with distant supervision.
result Significant improvements over state-of-the-art baselines.
DeepAISE predicts sepsis onset with high accuracy and low false alarms.
problem Early prediction of sepsis in ICU patients to improve clinical situational awareness.
method Recurrent neural survival model that combines clinical criteria and treatment policies.
result DeepAISE produces the most accurate predictions (AUC=0.90 and 0.87) and lowest false alarm rates (FAR=0.20 and 0.26) compared to baseline models.
The argument that the alarming level of Gini coefficient is 0.4 is very popular, especially in the media industry, all around the world for a long time. Although the 0.4 standard is widely accepted, the derivation of the value lacks rigid theoretical foundations. In fact, to the best of our knowledge, it is not based o…
Online anomaly detection in surveillance videos with false alarm rate bounds.
problem Lack of theoretical performance analysis and online decision making in anomaly detection.
method Proposes an online anomaly detection method with asymptotic bounds on false alarm rate.
result Demonstrates effectiveness on publicly available data sets, outperforming state-of-the-art algorithms.
Detects data drift in deep learning models using neural embeddings.
problem Detecting changes in data distribution in deep learning models.
method Formulates drift detection in a sequential decision framework and introduces a loss function to balance false alarms and quick detection.
result Demonstrates improved ability to balance false alarms and quick detection in change detection.
Optimal alarms detect vehicle collisions with theoretical and empirical validation.
problem Detecting dangerous vehicle collisions in real-time.
method Surveyed and compared three classes of collision detection techniques: Monte Carlo, deterministic approximations, and machine learning.
result Monte Carlo sampling is a robust solution for real-time collision detection despite its simplicity.
System identifies and responds to help queries in personal assistants.
problem Difficulty in remembering command structures for various tasks in personal assistants.
method Proposes a C-BiLSTM based classifier and semantic ANN module to detect and respond to help queries.
result System outperforms other approaches in returning relevant responses for help queries.
Improved outbreak detection using machine learning fusion of statistical algorithms.
problem Balancing detection of outbreaks with false alarms.
method Train a fusion classifier using p-values and additional features.
result Fusion classifier using p-values and additional features improves outbreak detection.
New benchmark uncovers hidden biases in LLMs that refuse to answer certain queries.
problem Evaluating fairness in LLMs, especially in sensitive applications.
method Silenced Bias Benchmark (SBB) using activation steering to reduce model refusals during QA.
result Exposes hidden unfair preferences in LLMs' latent space, distinguishing direct responses from underlying fairness issues.
Reduced-channel EEG systems struggle with artifact detection, highlighting the importance of referential channels.
problem Artifact detection in EEG signals with fewer channels.
method Investigated a deep learning algorithm, CNN-LSTM, on various channel configurations.
result False alarms increase dramatically when fewer channels are used, emphasizing the importance of referential channels.
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.
MI attacks often mislabel non-training samples, making them impractical.
problem MI attacks mislabel non-training samples, leading to high false positive rate.
method Analyzed new features like distance to decision boundary and gradient norms.
result MI attacks cannot achieve high accuracy and low false positive rate simultaneously.
Detects model changes in machine learning with empirical difference test.
problem Detect significant model changes between pre-change and post-change parameters.
method Constructs an empirical difference test (EDT) to approximate GLRT, with low computational complexity and false alarm constraint.
result EDT approximates GLRT and provides a method to set threshold for false alarm constraint.
KQT-EWMA monitors multivariate data streams online with flexible and practical change detection.
problem Online monitoring of multivariate data streams for detecting changes.
method Combines Kernel-QuantTree histogram and EWMA statistic for non-parametric monitoring.
result Controls Average Run Length (ARL0) while achieving comparable detection delays.
Extends FC-RAG to anytime-valid sequential coverage for language model swarms.
problem Maintain distribution-free coverage for a swarm of weak language models over time.
method Introduces Anytime-FC-RAG, a sequential extension with a summable calibration-deviation budget.
result Achieves time-uniform alarm validity and safety under predictable adaptive control.
Autoencoder detects subtle changes in time series data.
problem Detect abrupt changes in time series data with high accuracy.
method Autoencoder with time-invariant representation and postprocessing.
result Outperforms baseline methods on various data sets.
New method detects and locates changes in spatio-temporal point processes.
problem Detecting and localizing changes in spatio-temporal data.
method Score-based, likelihood-free approach estimating change time and region.
result The method provides theoretical guarantees on detection and localization accuracy.
We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on scores derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at alpha-…
Deep learning improves seizure detection in EEGs.
problem Challenges in automated seizure detection in EEGs due to low signal-to-noise ratio and confusion with artifacts.
method Evaluation of hybrid deep structures including Convolutional Neural Networks and Long Short-Term Memory Networks on the TUH EEG Seizure Corpus.
result 30% sensitivity at 7 false alarms per 24 hours using a novel recurrent convolutional architecture.
Detects harmful shifts without labels for model performance.
problem Detecting distribution shifts without access to labels.
method Uses a proxy derived from predictions of an error estimator.
result High power and false alarm control under various shifts.
Study on time reversal and last passage time of diffusions for credit risk management.
problem Credit risk management using leverage process and alarming levels.
method Analysis of time reversal, last passage time, and h-transform of linear diffusions. result Developed a new risk management framework for companies.
Balancing graph summarization and change detection in streaming data.
problem Balancing compression rate in graph summarization and accuracy in change detection.
method Introducing a probabilistic hierarchical latent variable model and optimizing parameters based on the minimum description length principle to balance the trade-off.
result Guaranteed suppression of Type I error probability (false alarms) in change detection.
Financial markets are well known for their dramatic dynamics and consequences that affect much of the world's population. Consequently, much research has aimed at understanding, identifying and forecasting crashes and rebounds in financial markets. The Johansen-Ledoit-Sornette (JLS) model provides an operational framew…
PITMonitor monitors model calibration over time with formal error guarantees.
problem Fixed-sample tests applied to models over time can lead to false alarms.
method PITMonitor uses mixture e-processes to detect distributional shifts in probability integral transforms.
result PITMonitor achieves competitive detection rates on river's FriedmanDrift benchmark.
We propose a non-parametric anomaly detection algorithm for high dimensional data. We score each datapoint by its average K-NN distance, and rank them accordingly. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as an anomaly…
We propose a non-parametric anomaly detection algorithm for high dimensional data. We first rank scores derived from nearest neighbor graphs on n-point nominal training data. We then train limited complexity models to imitate these scores based on the max-margin learning-to-rank framework. A test-point is declared as…
A new local outlier detection method using KDE and RDOS.
problem Detecting local outliers in data.
method Local kernel density estimation (KDE) and Relative Density-based Outlier Score (RDOS).
result The method outperforms state-of-the-art methods in experiments.
A new method detects changes in data sequences by comparing backward and forward confidence sequences.
problem Detecting changes in data sequences over time.
method Constructing and comparing backward and forward confidence sequences.
result The method successfully detects changes with strong nonasymptotic guarantees.
Machine learning detects drilling anomalies, reducing accidents and costs.
problem Detecting and preventing accidents during directional drilling.
method Time-series comparison using machine learning and Gradient Boosting classification.
result The model detects half of the anomalies with about 0.53 false alarms per day.
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
problem Dynamic distribution shifts challenge real-world machine learning systems' risk assurances.
method Sequential hypothesis testing with 'testing by betting' to detect risk violations.
result Effective real-time risk monitoring under various unknown shifts.
Deep RL improves cellular network fault management and performance.
problem Fault management and radio performance improvement in outdoor cellular networks.
method Deep Q-Learning for self-organizing networks fault management.
result The proposed algorithm learns to clear alarms and improve radio performance better than existing methods.
Novel online graph-based method detects changes in high-dimensional data.
problem Challenges in detecting changes in high-dimensional data.
method Graph-based similarity measure derived from graph-spanning ratio.
result High detection power and controlled false alarm rate for high-dimensional data.
Reduces change detection to estimation using confidence sequences.
problem Detecting changes in data streams with minimal delay and false alarms.
method Reduction from sequential change detection to sequential estimation using confidence sequences.
result Change detection scheme with minimal structural assumptions and strong guarantees.
The paper reviews methods for testing randomness and exchangeability in sequential data.
problem Testing the randomness and exchangeability of sequential data.
method Conformal martingales for online testing of randomness and exchangeability.
result Valid and efficiency results for various conformal martingale-based procedures.
New algorithm detects concept drifts from unlabeled data streams.
problem Detecting concept drifts from unlabeled data streams.
method Margin Density Drift Detection (MD3) algorithm.
result MD3 reliably detects drifts with fewer false alarms.
Geometric observables detect financial regime shifts with high accuracy.
problem Detecting regime shifts in financial markets.
method Extracted four geometric observables from equity-index returns and evaluated them against various baseline methods.
result The Berry Phase Rate achieves an unbiased out-of-sample median Cohen's d of 0.72, significantly reducing false alarms.