arXiv research

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

This paper studies the properties of the optimal portfolio-consumption strategies in a {finite horizon} robust utility maximization framework with different borrowing and lending rates. In particular, we allow for constraints on both investment and consumption strategies, and model uncertainty on both drift and volatil…

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

New loss function handles uncertain constraints in CSLO problems.

Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.

In this paper we consider an interval portfolio selection problem with uncertain returns and introduce an inclusive concept of satisfaction index for interval inequality relation. Based on the satisfaction index, we propose an approach to reduce the interval programming problem with uncertain objective and constraints …

The paper tackles online resource allocation with uncertain coefficients and chance constraints.

Synthesizes sensor likelihoods to enforce accuracy constraints in uncertain systems.

Paper optimizes financial trading strategies under uncertain market conditions.

Method estimates posterior model for boundary value problems with uncertain constraints.

Paper proposes online optimization for uncertain systems using machine learning and DRO.

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

Paper develops online learning-based risk-averse MPC for uncertain systems.

A new RL model ensures safe learning in uncertain environments.

Investment and consumption strategy optimized under uncertain conditions.

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited …

Safe learning in uncertain systems with state measurements and optimization.

Develops a new method for optimizing with uncertain data.

Develops a new solver for optimizing with stochastic dominance constraints.

This paper considers utility indifference valuation of derivatives under model uncertainty and trading constraints, where the utility is formulated as an additive stochastic differential utility of both intertemporal consumption and terminal wealth, and the uncertain prospects are ranked according to a multiple-priors …

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

New method calculates Shapley values for uncertain functions.

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…

Bayesian method for estimating inputs leading to specific probability outputs.

New algorithms control loss and constraints in uncertain, changing environments.

Quantum methods model uncertain volatility in financial markets.

New model predicts dynamic volatility in uncertain financial markets.

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 method designs fairer transport plans with uncertainty.

New algorithm for uncertain time series classification.

This paper studies robust forward investment and consumption preferences within a zero-volatility context. Different from previous works, we consider an incomplete financial market model due to general investment portfolio constraints. We provide a new PDE characterization and a novel semi-explicit saddle-point constru…

Investor optimizes investment and consumption under uncertain market conditions with constraints.

The paper studies robust risk measures with linear penalties under uncertain distributions.

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.

Interactive steering improves hierarchical clustering for diverse user needs.

Model quantifies uncertainty's impact on European option prices.

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

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

Algorithm ensures safe optimization under unknown constraints.

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffus…

This paper tackles JSSP with uncertain task durations using DRL.

This paper first describes a class of uncertain stochastic control systems with Markovian switching, and derives an Itô-Liu formula for Markov-modulated processes. And we characterize an optimal control law, which satisfies the generalized Hamilton-Jacobi-Bellman (HJB) equation with Markovian switching. Then, by using …

This paper improves learning uncertain Bayesian networks from incomplete data.

Proposes a new uncertain volatility model with worst-case scenario analysis.