arXiv research

Safe Bayesian optimization method using information theory.

ARTEO algorithm optimizes safety-critical systems with uncertainty.

Safe reinforcement learning framework using optimal transport for robustness.

Enforcing safety is a key aspect of many problems pertaining to sequential decision making under uncertainty, which require the decisions made at every step to be both informative of the optimal decision and also safe. For example, we value both efficacy and comfort in medical therapy, and efficiency and safety in robo…

Risk-averse model uncertainty framework for safe reinforcement learning.

Investigates safe decision-making in interactive environments.

Algorithm reduces regret in safe Bayesian optimization with monotonicity constraints.

Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment. Combined with the rule-based constraints, a Deep Q-Network (DQN) based method is applied for autonomous driving lane change decision-making task in this study. Through the combination of high-level …

An important problem in sequential decision-making under uncertainty is to use limited data to compute a safe policy, i.e., a policy that is guaranteed to perform at least as well as a given baseline strategy. In this paper, we develop and analyze a new model-based approach to compute a safe policy when we have access …

This paper proposes a method to safely adjust exploration in RL to satisfy constraints.

Examines fairness in ML for health, highlighting its importance and challenges.

A new RL model ensures safe learning in uncertain environments.

Safety is a desirable property that can immensely increase the applicability of learning algorithms in real-world decision-making problems. It is much easier for a company to deploy an algorithm that is safe, i.e., guaranteed to perform at least as well as a baseline. In this paper, we study the issue of safety in cont…

Study best arm identification with safety constraints in bandit problems.

New algorithm optimizes reward while ensuring safety in complex decision-making problems.

Proposes Constrained Q-learning for reinforcement learning with constraints.

Develops new methods for risk-aware decision-making in medical bandits.

In many safety-critical applications such as autonomous driving and surgical robots, it is desirable to obtain prediction uncertainties from object detection modules to help support safe decision-making. Specifically, such modules need to estimate the probability of each predicted object in a given region and the confi…

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance it is crucial to guarantee the safety of an agent during training as well as deployment (e.g. a robot…

We introduce the safe linear stochastic bandit framework---a generalization of linear stochastic bandits---where, in each stage, the learner is required to select an arm with an expected reward that is no less than a predetermined (safe) threshold with high probability. We assume that the learner initially has knowledg…

In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…

CoCoRL learns safe constraints from demonstrations with unknown rewards.

SafePILCO is a Python tool for safe reinforcement learning.

New risk metric for AI systems reduces safety risks with minimal data.

Safe learning of stochastic dynamics with safety constraints.

High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be \emph{safe}. In this …

We show that when a third party, the adversary, steps into the two-party setting (agent and operator) of safely interruptible reinforcement learning, a trade-off has to be made between the probability of following the optimal policy in the limit, and the probability of escaping a dangerous situation created by the adve…

Study on neural networks to identify redundancy issues in safe machine learning.

Future Connected and Automated Vehicles (CAV), and more generally ITS, will form a highly interconnected system. Such a paradigm is referred to as the Internet of Vehicles (herein Internet of CAVs) and is a prerequisite to orchestrate traffic flows in cities. For optimal decision making and supervision, traffic centres…

We study safe screening for metric learning. Distance metric learning can optimize a metric over a set of triplets, each one of which is defined by a pair of same class instances and an instance in a different class. However, the number of possible triplets is quite huge even for a small dataset. Our safe triplet scree…

Bitcoin fails to prove safe haven status during pandemic.

Proposes a method to accelerate safe sequential learning using offline data.

Meta-learning priors improves safe Bayesian optimization.

Revel tackles safe exploration in RL with verified symbolic policies.

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

Safe-House secures DeFi by limiting losses and enhancing security.

In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual observations incomplete and unreliable. Moreover, in many situations it is preferable to delay a decision rather than run the risk of making …

OSIL learns safe policies from unsafe demonstrations.

We study the problem of safe learning and exploration in sequential control problems. The goal is to safely collect data samples from operating in an environment, in order to learn to achieve a challenging control goal (e.g., an agile maneuver close to a boundary). A central challenge in this setting is how to quantify…

The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either fo…

A new screening rule 'dynamic Sasvi' improves sparse optimization speed.

Crypto-assets perform better than gold as safe-havens during market crashes.

Safe autonomous decisions made with machine learning predictions using Conformal Decision Theory.

Safe sample screening improves RSVM performance without sacrificing accuracy.

Safe screening rules reduce computation time in logistic regression with $\ell_0-\ell_2$ regularization.

In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster s…

SAMBA improves safe reinforcement learning with active exploration metrics.

Safe-M$^3$-UCRL learns safe policies for multi-agent systems with global constraints.