arXiv research

Survey on risk-aware multi-armed bandits for better decision-making.

Study uses RL to hedge financial derivatives, showing robust strategies outperform non-robust ones.

Despite the simplicity and intuitive interpretation of Minimum Mean Squared Error (MMSE) estimators, their effectiveness in certain scenarios is questionable. Indeed, minimizing squared errors on average does not provide any form of stability, as the volatility of the estimation error is left unconstrained. When this v…

We solve non-Markovian optimal switching problems in discrete time on an infinite horizon, when the decision maker is risk aware and the filtration is general, and establish existence and uniqueness of solutions for the associated reflected backward stochastic difference equations. An example application to hydropower …

Risk-aware linear bandits optimize against adverse outcomes.

A risk-aware RL approach using RDEU and Wasserstein ball for robust performance.

Unified framework for risk-aware policy learning in contextual bandits.

Active learning from demonstration allows a robot to query a human for specific types of input to achieve efficient learning. Existing work has explored a variety of active query strategies; however, to our knowledge, none of these strategies directly minimize the performance risk of the policy the robot is learning. U…

Study improves summarization reliability in risky scenarios.

Paper proposes a risk-aware decision-making framework for real-world sequential decisions.

CREDIT learns to master pair trading with risk-aware RL, outperforming existing methods.

New method uses DistRL to estimate entire payoff distribution for financial derivatives.

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

Federated Learning tackles limited user participation with a new risk-aware approach.

SAFER improves personalized treatment recommendations for dynamic clinical contexts.

Sequential portfolio selection has attracted increasing interests in the machine learning and quantitative finance communities in recent years. As a mathematical framework for reinforcement learning policies, the stochastic multi-armed bandit problem addresses the primary difficulty in sequential decision making under …

Study enhances financial forecasting with machine learning and fuzzy MCDM.

Improves reinforcement learning policies for robustness.

A new framework for risk-aware multi-armed bandits tackles volatile environments.

SVAT reduces investment risks by making stock models sensitive to adversarial perturbations.

Paper uses DRL to optimize portfolios, balancing risk and return.

WRAAC uses Wasserstein distance for robust reinforcement learning.

Optimizes trading portfolios considering risk and profit.

Locus scores predictions for risk, reducing large-loss events.

Study proposes optimal risk-aware interest rates for crypto lending protocols.

WaveLSFormer learns profitable trading policies from financial time series data.

HRT uses bi-level reinforcement learning to optimize stock selection and execution in multi-asset equity markets.

Develops a framework for robust RL with dynamic risk measures.

FinHEAR combines LLMs with human expertise for better financial decision-making.

This paper tackles risk-aware energy scheduling for MEC networks with microgrids.

DeepScalper uses RL to capture intraday trading opportunities, balancing risk and profit.

ClauseLens uses reinforcement learning to price reinsurance treaties transparently and auditably.

A new game-theoretic approach balances downside risk with expected reward.

We present $\textit{Free-MESSAGE}^{p}$, the first zeroth-order algorithm for (weakly-)convex mean-semideviation-based risk-aware learning, which is also the first three-level zeroth-order compositional stochastic optimization algorithm whatsoever. Using a non-trivial extension of Nesterov's classical results on Gaussia…

In the field of reinforcement learning there has been recent progress towards safety and high-confidence bounds on policy performance. However, to our knowledge, no practical methods exist for determining high-confidence policy performance bounds in the inverse reinforcement learning setting---where the true reward fun…

We develop a statistical framework to benchmark and select large language models based on their risks.

Stochastic GD converges linearly for CV@R learning under certain conditions.

RAGIC predicts stock intervals with risk considerations, improving prediction accuracy and coverage.

Supporting ultra-reliable low-latency communications (URLLC) is a major challenge of 5G wireless networks. Stringent delay and reliability requirements need to be satisfied for both scheduled and non-scheduled URLLC traffic to enable a diverse set of 5G applications. Although physical and media access control layer sol…

This paper extends the MAB problem to consider risk-reward tradeoffs.

Deep Q-learning agent outperforms traditional hedging in S&P 500 options.

FinStressTS creates synthetic benchmarks for financial forecasting, revealing model weaknesses.

Paper improves ETF tail-risk monitoring reliability.

This paper improves parameter estimation for autonomous systems with unmodeled dynamics.

Optimized Sharpe Ratio for better risk-adjusted decision-making in multi-armed bandits.

The paper analyzes risk estimation methods and derives bounds for OCE risk.

We consider a continuous time Principal-Agent model on a finite time horizon, where we look for the existence of an optimal contract both parties agreed on. Contrary to the main stream, where the principal is modelled as risk-neutral, we assume that both the principal and the agent have exponential utility, and are ris…

We propose a new definition for tameness within the model of security prices as Itô processes that is risk-aware. We give a new definition for arbitrage and characterize it. We then prove a theorem that can be seen as an extension of the second fundamental theorem of asset pricing, and a theorem for valuation of contin…