arXiv research

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

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…

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

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

Risk-aware linear bandits optimize against adverse outcomes.

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

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

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

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

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

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

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 …

Study enhances financial forecasting with machine learning and fuzzy MCDM.

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 …

Improves reinforcement learning policies for robustness.

Study improves summarization reliability in risky scenarios.

SAFER improves personalized treatment recommendations for dynamic clinical contexts.

WRAAC uses Wasserstein distance for robust reinforcement learning.

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

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

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.

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

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

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…

WaveLSFormer learns profitable trading policies from financial time series data.

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

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

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

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…

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

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

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

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…

Optimizes trading portfolios considering risk and profit.

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

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

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

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

Hi-fi priors enhance BNNs by learning flexible activations.

The paper tackles catastrophic risk in reinforcement learning using extreme value theory.

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

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

In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established …

Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estimated preference order is recommended to the end user in the decreasing …

MVPI framework optimizes risk in reinforcement learning, improving performance in robot simulations.

New risk metric for RL in finance considers time splits of returns.