arXiv research

Bitcoin fails to prove safe haven status during pandemic.

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

During times of extreme market turmoil, it is acknowledged that there is a tendency towards "flight to safety". A strong (weak) safe haven is defined as an asset that has a significant positive (negative) return in periods where another asset is in distress, while hedge has to be negatively correlated (uncorrelated) on…

The study improves gold's role as a hedge and safe haven using new correlation measures.

This paper provides an innovative perspective on the role of gold as a hedge and safe haven. We use a quantile-on-quantile regression approach to capture the dependence structure between gold returns and changes in uncertainty under different gold market conditions, while considering the nuances of uncertainty levels. …

Although Bitcoin has long been dominant in the crypto scene, it is certainly not alone. Ether is another cryptocurrency related project that has attracted an intensive attention because of its additional features. This study seeks to test whether these cryptocurrencies differ in terms of their volatile and speculative …

Our study proposes a new currency system to protect wealth from over-issued fiat and stablecoins.

The study examines tail dependence between global economic uncertainty and BRICS currencies using high-frequency data.

Improved MF-DFA model analyzes precious metals market efficiency and multifractality.

Quant firms manipulate stock markets overnight and intraday.

Study examines cryptoasset service providers in Austria, revealing global integration and distinct responses to market shocks.

Unified framework maps financial market dynamics using TE and KM, revealing directional information flow.

Bitcoin fails to function as a stable currency or store of value.

Safe linear stochastic bandits ensure safe exploration with optimal regret.

CoCoRL learns safe constraints from demonstrations with unknown rewards.

SafePILCO is a Python tool for safe reinforcement learning.

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.

Cryptocurrencies are increasingly correlated with traditional financial markets.

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…

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.

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…

Corporate governance struggles to curb fraud in a globalized economy.

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.

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

Safe Bayesian optimization method using information 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.

Adapts safe policies for exploration in high-risk settings.

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

Bandit algorithms have various application in safety-critical systems, where it is important to respect the system constraints that rely on the bandit's unknown parameters at every round. In this paper, we formulate a linear stochastic multi-armed bandit problem with safety constraints that depend (linearly) on an unkn…

Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations. These situations sometimes require us to avoid certain outcomes at all cost, for example because they are poisonous, harmful, or otherwise dangerous. We test…

Safe actions learned in finite trials, without infinite exploration.

Safe imitation learning with a safety layer for flexible training.

In Interactive Machine Learning (IML), we iteratively make decisions and obtain noisy observations of an unknown function. While IML methods, e.g., Bayesian optimization and active learning, have been successful in applications, on real-world systems they must provably avoid unsafe decisions. To this end, safe IML algo…

Safe RL with binary feedback using SABRE algorithm.

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…