arXiv research

Deep learning identifies precipitation clouds from all-sky camera data.

Python tool creates machine-learning-ready solar dataset.

CNNs improve signal-background classification in particle physics experiments.

On 2 November 2009, the Financial Bubble Experiment was launched within the Financial Crisis Observatory (FCO) at ETH Zurich (\url{http://www.er.ethz.ch/fco/}). In that initial report, we diagnosed and announced three bubbles on three different assets. In this latest release of 23 December 2009 in this ongoing experime…

Macroeconomic theories of growth and wealth distribution have an outsized influence on national and international social and economic policies. Yet, due to a relative lack of reliable, system wide data, many such theories remain, at best, unvalidated and, at worst, misleading. In this paper, we introduce a novel econom…

RLVR maintains safety while improving reasoning capabilities in LLMs.

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…

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.

Paper defines and quantifies safety risks in deep neural networks.

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…

CNN improves neutrino event reconstruction in IceCube DeepCore.

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.

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning. In this work, we leverage graph neural networks to improve signal detection in the IceCube neutrino ob…

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…

In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature extractor for clustering tasks. The trained embedding function transfers knowledge from the domain of a labeled set of morphologically-disti…

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.

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

A new method reduces the computational burden of safety alignment for large language models.

Urban traffic systems worldwide are suffering from severe traffic safety problems. Traffic safety is affected by many complex factors, and heavily related to all drivers' behaviors involved in traffic system. Drivers with aggressive driving behaviors increase the risk of traffic accidents. In order to manage the safety…

ARTEO algorithm optimizes safety-critical systems with uncertainty.

Synthesizes machine learning applications in reliability and safety.

The paper tackles safe exploration in RL by a conservative safety critic.

High-dimensional models pose both safety benefits and risks.