arXiv research

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.

New model predicts dynamic volatility in uncertain financial markets.

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep r…

We use copulas to improve SLAM in uncertain environments.

The paper tackles mean-variance analysis in Bayesian optimization under uncertainty.

Proposes a recursive MPC scheme with probabilistic safety guarantees for uncertain dynamic systems.

In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transact…

HRM-Agent learns to navigate dynamic mazes using reinforcement learning.

The Surprise index assesses autonomous systems' competency in uncertain environments.

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…

IDT learns human preferences from uncertain decisions, even when humans are suboptimal.

Safe learning in uncertain systems with state measurements and optimization.

Agent learns to navigate uncertain 3D maps using a hybrid planner.

Survey of RL methods for optimizing power grid topologies.

Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.

A new RL model ensures safe learning in uncertain environments.

Greenhouse environment is the key to influence crops production. However, it is difficult for classical control methods to give precise environment setpoints, such as temperature, humidity, light intensity and carbon dioxide concentration for greenhouse because it is uncertain nonlinear system. Therefore, an intelligen…

A framework for navigating environments with spatially correlated obstacles and uncertain blockage status.

We develop a framework for interacting with uncertain environments in reinforcement learning (RL) by leveraging preferences in the form of utility functions. We claim that there is value in considering different risk measures during learning. In this framework, the preference for risk can be tuned by variation of the p…

Reinforcement Learning (RL) has demonstrated a great potential for automatically solving decision-making problems in complex uncertain environments. RL proposes a computational approach that allows learning through interaction in an environment with stochastic behavior, where agents take actions to maximize some cumula…

Bayesian method optimizes uncertain constraints in black-box function optimization.

New RL agent learns sparse rewards efficiently.

Risk-averse model uncertainty framework for safe reinforcement learning.

Paper shows hard computational limits for invariant causal prediction.

Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.

Study option pricing in sideways markets and target zones.

The paper explores how to handle uncertain evidence in probabilistic models.

New method calculates Shapley values for uncertain functions.

New mirror maps improve PMD performance in reinforcement learning.

In this paper, within the framework of uncertainty theory, the valuation of equity warrants is investigated. Different from the methods of probability theory, the equity warrants pricing problem is solved by using the method of uncertain calculus. Based on the assumption that the firm price follows an uncertain differe…

Quantum methods model uncertain volatility in financial markets.

Strategic valuation of efficient and well-timed network investments under uncertain electricity market environment has become increasingly challenging, because there generally exist multiple interacting options in these investments, and failing to systematically consider these options can lead to decisions that underva…

The target of this paper is to consider model the risky asset price on the financial market under the Knightian uncertainty, and pricing the ask and bid prices of the uncertain risk. We use the nonlinear analysis tool, i.e., G-frame work [26], to construct the model of the risky asset price and bid-ask pricing for the …

Study calculates Bayes risk for semi-supervised learning with uncertain labels.

New algorithm for uncertain time series classification.

Robustness is important for sequential decision making in a stochastic dynamic environment with uncertain probabilistic parameters. We address the problem of using robust MDPs (RMDPs) to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution is det…

Policy shifts between Trump and Biden impact ESG investments, creating volatility.

A method for accurate pricing of multidimensional derivatives under uncertain volatility.

The paper addresses portfolio allocation with uncertain covariance matrices, finding a logarithmic risk dependence.

Bayesian Gaussian process models handle uncertain data locations in PDE approximations.

Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.

Model quantifies uncertainty's impact on European option prices.

New algorithms for collaborative learning in uncertain, decentralized environments.

The paper tackles robust control with uncertain dependence using data-driven methods.

This paper introduces sample-averaged Q-learning for better RL performance.

New framework for robust reinforcement learning policies in uncertain environments.

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

We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained during execution of one task has value for the execution of another task. Consequently, the agent is intrinsically motivated to explore its envi…