arXiv research

ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.

Unified approach detects traffic conflicts across various interactions.

Predictive models can fail to generalize from training to deployment environments because of dataset shift, posing a threat to model reliability and the safety of downstream decisions made in practice. Instead of using samples from the target distribution to reactively correct dataset shift, we use graphical knowledge …

Suitability filter detects model performance degradation in real-world deployment.

During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing re…

Proceed adapts models proactively against concept drift in online time series forecasting.

Proposes a framework to assess model robustness to dataset shifts.

This research improves capital efficiency and impermanent loss in cryptocurrency markets using multi-token trading pools.

Proposes proactive bed requests to reduce ED boarding and patient wait times.

This paper evaluates targeted data poisoning attacks by focusing on the hardest samples, improving evaluation and defense strategies.

Predict real-time crash risks during hurricane evacuations using connected vehicle data.

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock ma…

HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.

RLVR maintains safety while improving reasoning capabilities in LLMs.

Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.

In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these manufacturing processes are difficult. Thus, the managers need more proactive approa…

The paper discusses safety assessment for AI systems, focusing on machine learning models.

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we revi…

Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of …

Fine-tuning LLMs improves capability but harms safety, study finds.

RAGuard improves safety in LLMs for offshore wind maintenance.

Survey of algorithms for testing AI-driven CPS safety.

Safe imitation learning with a safety layer for flexible training.

Deep learning methods are widely regarded as indispensable when it comes to designing perception pipelines for autonomous agents such as robots, drones or automated vehicles. The main reasons, however, for deep learning not being used for autonomous agents at large scale already are safety concerns. Deep learning appro…

This paper formalizes AI safety using hypothesis testing in GenAI.

A new algorithm for identifying the best arm in linear feedback with safety constraints.

Safety filter for unknown discrete-time systems with learned models and noise covariance.

SafeML monitors ML systems for safety and security risks.

Prophet predicts device qualities for FL to reduce training latency.

Paper defines and quantifies safety risks in deep neural networks.

Climate-contingent finance helps adapt to uncertain climate risks.

Robust policies improve ICU transfer outcomes by predicting patient deterioration.

Study best arm identification with safety constraints in bandit problems.

Failures are challenging for learning to control physical systems since they risk damage, time-consuming resets, and often provide little gradient information. Adding safety constraints to exploration typically requires a lot of prior knowledge and domain expertise. We present a safety measure which implicitly captures…

Safe reinforcement learning tackles safety constraints with linear approximations.

Enhances safety of 3D object detection neural networks.

Each year, around 6 million car accidents occur in the U.S. on average. Road safety features (e.g., concrete barriers, metal crash barriers, rumble strips) play an important role in preventing or mitigating vehicle crashes. Accurate maps of road safety features is an important component of safety management systems for…

CARL safely adapts RL agents for safety-critical tasks.

A novel approach for safe offline RL using latent safety constraints.

New method improves safety analytics by addressing imbalanced data issues.

LLM safety alignment explained as divergence estimation.

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine le…

Safe Bayesian Optimization algorithms are improved to ensure safety in real-world applications.

Researchers introduce a method to assess the safety of interpretable machine learning models.

New method provides scalable safety guarantees for RL agents.

By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and aca…

Algorithm minimizes regret while adhering to unknown safety constraints.

The paper shows why AI safety doesn't generalize across tasks.