arXiv research

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

Safe linear bandits over unknown polytopes avoid safety violations and suboptimal actions.

CRC improves multivariate forecasting accuracy without risking performance degradation.

Develops algorithms for CCBs with non-linear costs, improving safety and performance.

Logarithmic regret strategies for safe multi-armed bandits with safety risk constraints.

Extends neural net safety guarantees by proving structural properties.

The use of machine learning (ML) is on the rise in many sectors of software development, and automotive software development is no different. In particular, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are two areas where ML plays a significant role. In automotive development, safety is…

Safe-EF improves federated learning for non-smooth, constrained optimization.

ASE safely explores unknown MDPs with unknown dynamics, improving sample efficiency.

New bounds on trajectory safety in training models with Langevin Dynamics.

Paper presents LSTMMDN for hourly bike flow estimation in Copenhagen.

A decentralized algorithm minimizes cumulative regret in stochastic linear bandits with safety constraints.

RLVR maintains safety while improving reasoning capabilities in LLMs.

This review explores ML and DL techniques for detecting distracted driving across various modalities.

We apply Deep Q-network (DQN) with the consideration of safety during the task for deciding whether to conduct the maneuver. Furthermore, we design two similar Deep Q learning frameworks with quadratic approximator for deciding how to select a comfortable gap and just follow the preceding vehicle. Finally, a polynomial…

Research tackles safety of deep learning in safety-critical tasks.

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…

Study examines uncertainty in adversarially trained models and proposes improved AT methods.

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.

This paper formalizes AI safety using hypothesis testing in GenAI.

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

SafeML monitors ML systems for safety and security risks.

Paper defines and quantifies safety risks in deep neural networks.

Study best arm identification with safety constraints in bandit problems.

ALMANACS benchmarks explainability methods on simulatability.

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.

Study probabilistic safety of BNNs under adversarial attacks.

Safe learning of stochastic dynamics with safety constraints.

Machine learning algorithms are increasingly influencing our decisions and interacting with us in all parts of our daily lives. Therefore, just like for power plants, highways, and myriad other engineered sociotechnical systems, we must consider the safety of systems involving machine learning. In this paper, we first …

Unified control theory and machine learning for safety in uncertain systems.