arXiv research

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

Paper defines ε-Safe Decision Regions for exponential family distributions and approximates them for unbalanced data.

RYU framework constructs safe regions for optimization problems.

A new screening test for Lasso improves solution speed.

Deep neural networks have become widely used, obtaining remarkable results in domains such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bio-informatics, where they have produced results comparable to human experts. However, th…

Safe RL for autonomous vehicles using PCPO with trust regions and parallel learners.

New method avoids failures in physics-constrained systems using active learning.

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 …

Safe learning of stochastic dynamics with safety constraints.

In this paper, the problem of safe global maximization (it should not be confused with robust optimization) of expensive noisy black-box functions satisfying the Lipschitz condition is considered. The notion "safe" means that the objective function $f(x)$ during optimization should not violate a "safety" threshold, for…

Combines Lyapunov functions with controller synthesis for safe control policies.

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…

Safe Bayesian optimization method using information theory.

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

Safe RL with binary feedback using SABRE algorithm.

Algorithm reduces regret in safe Bayesian optimization with monotonicity constraints.

Screening rules allow to early discard irrelevant variables from the optimization in Lasso problems, or its derivatives, making solvers faster. In this paper, we propose new versions of the so-called $\textit{safe rules}$ for the Lasso. Based on duality gap considerations, our new rules create safe test regions whose d…

Reinforcement learning is a powerful paradigm for learning optimal policies from experimental data. However, to find optimal policies, most reinforcement learning algorithms explore all possible actions, which may be harmful for real-world systems. As a consequence, learning algorithms are rarely applied on safety-crit…

Safe RL in linear systems achieves $\sqrt{T}$-regret.

Gaussian Processes (GPs) are widely employed in control and learning because of their principled treatment of uncertainty. However, tracking uncertainty for iterative, multi-step predictions in general leads to an analytically intractable problem. While approximation methods exist, they do not come with guarantees, mak…

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

Simplified screening tests for data points in optimization.

Bayesian optimization sped up with model approximations for safe online system optimization.

Safe learning in uncertain systems with state measurements and optimization.

Framework for safely updating machine learning models.

A new error bound improves safety in Bayesian optimization.

New bounds for kernel regression under non-Gaussian noise.

This work establishes safe reinforcement learning for LQR with nonlinear baselines.

This paper focusses on "safe" screening techniques for the LASSO problem. Motivated by the need for low-complexity algorithms, we propose a new approach, dubbed "joint" screening test, allowing to screen a set of atoms by carrying out one single test. The approach is particularized to two different sets of atoms, respe…

Despite being very effective in several classification tasks, Dynamic Ensemble Selection (DES) techniques can select classifiers that classify all samples in the region of competence as being from the same class. The Frienemy Indecision REgion DES (FIRE-DES) tackles this problem by pre-selecting classifiers that correc…

Tail-Safe hedging uses reinforcement learning with a safety layer to manage financial risks.

VaR-CPO optimizes VaR-constrained RL problems with conservative policy updates.

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…

CoCoRL learns safe constraints from demonstrations with unknown rewards.

SafePILCO is a Python tool for safe reinforcement learning.

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.

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…

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…

Modeling influenza spread using feature engineering and international flow deconvolution.

Bitcoin fails to prove safe haven status during pandemic.

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.

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…