arXiv research

New risk measures for quantiles under ambiguity improve risk sharing.

We study ranking quantilized mean-field games to select top-performing agents.

The paper addresses risk sharing and variability measures among agents with general risk preferences.

QR-MIX models joint state-action values as a distribution to handle randomness in MARL.

In this paper we consider the problem of the quantile hedging from the point of view of a better informed agent acting on the market. The additional knowledge of the agent is modelled by a filtration initially enlarged by some random variable. By using equivalent martingale measures introduced in Amendinger (2000) and …

When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents. Recently proposed deep multi-agent reinforcement learning methods have tried to mitigate this non-stationarity by attempting t…

With model uncertainty characterized by a convex, possibly non-dominated set of probability measures, the agent minimizes the cost of hedging a path dependent contingent claim with given expected success ratio, in a discrete-time, semi-static market of stocks and options. Based on duality results which link quantile he…

Framework uses IRL and RL to elicit and optimize risk preferences robustly to noise.

Paper proposes a new method for better estimating continuous distributions in RL.

New method for risk quantification using quantile processes and measure distortions.

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…

Study quantile reward identification with 1-bit feedback constraints.

Enhances GFlowNets with distributional approach for risk-sensitive policies.

A new RL method improves performance on Atari games without complex techniques.

This note gives a short, self-contained, proof of a sharp connection between Gittins indices and Bayesian upper confidence bound algorithms. I consider a Gaussian multi-armed bandit problem with discount factor $γ$. The Gittins index of an arm is shown to equal the $γ$-quantile of the posterior distribution of the arm'…

Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.

New federated conformal prediction method addresses label shift for uncertainty quantification.

Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting. However, explicitly parameterized policies are limited by the scope of the chosen parametric probability distribution. We show t…

Study on pairwise counter-monotonicity, a type of negative dependence.

The paper studies risk-sharing allocations for risk-seeking agents using a common distortion risk measure.

Investigates methods to regularize quantile regression for accurate predictions.

A new method avoids quantile crossing in time series forecasting.

Paper finds robust $Λ$-quantiles equal to extremal distributions.

SCQRNN prevents quantile crossing and improves computational efficiency.

Axiomatizes $Λ$-quantiles, a generalization of quantiles.

Develops a method to ensure accurate quantile forecasts across multiple levels.

Develops quantile diffusions for risk analysis in continuous time.

Proposes a method to estimate conditional quantiles using both high-fidelity and low-fidelity data.

Sequential quantile estimation refers to incorporating observations into quantile estimates in an incremental fashion thus furnishing an online estimate of one or more quantiles at any given point in time. Sequential quantile estimation is also known as online quantile estimation. This area is relevant to the analysis …

The paper proposes a method for predicting equity premium using penalized quantile regression.

Supervised learning is an active research area, with numerous applications in diverse fields such as data analytics, computer vision, speech and audio processing, and image understanding. In most cases, the loss functions used in machine learning assume symmetric noise models, and seek to estimate the unknown function …

Improved conformalized quantile regression for adaptive prediction intervals.

Proposes a deep learning method to ensure non-crossing quantiles in conditional distributions.

Smoothed SGD improves quantile estimation without crossing curves.

Private estimation of many quantiles using differential privacy.

Bayesian method improves quantile estimation and subset selection.

New quantile methods improve uncertainty quantification across various models.

This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.

ConquerNet smooths quantile regression for deep learning with minimax guarantees.

Improved quantile estimation model for VaR.

We develop quantile regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk ma…

Paper proposes a method to estimate multiple dynamic quantiles jointly.

A new method forecasts financial tail risks by combining and weighting quantiles.

This paper examines quantile dependence between international stock markets and evaluates its use for improving volatility forecasting. First, we analyze quantile dependence and directional predictability between the US stock market and stock markets in the UK, Germany, France and Japan. We use the cross-quantilogram, …

Study efficient algorithms for one-shot federated conformal prediction.

IQ-BART models conditional quantiles using a non-parametric Bayesian approach.

The paper introduces a new method for forecasting financial risk using quantile-based modeling.

Paper introduces arctan pinball loss for XGBoost quantile regression.