arXiv research

Novel algorithms for online learning with uncertain feedback graphs reduce regret.

We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…

Algorithm learns fair division from noisy feedback in uncertain markets.

This paper tackles combinatorial optimization under uncertainty with limited feedback.

Paper studies CLO with partial feedback, improving decision-making in uncertain contexts.

Safe RL with binary feedback using SABRE algorithm.

New algorithms learn stable matchings from uncertain user preferences.

Optimization is becoming increasingly common in scientific and engineering domains. Oftentimes, these problems involve various levels of stochasticity or uncertainty in generating proposed solutions. Therefore, optimization in these scenarios must consider this stochasticity to properly guide the design of future exper…

New method for identifying best designs in vector optimization with uncertain feedback.

This work proposes a new method for simultaneous probabilistic identification and control of an observable, fully-actuated mechanical system. Identification is achieved by conditioning stochastic process priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous w…

AGFN improves causal discovery by integrating expert feedback and handling latent confounding.

Optimal investment strategy with expert opinions in uncertain conditions.

Paper addresses online alignment of large language models under uncertain preference feedback.

Improved DPO framework penalizes preference uncertainty to avoid overoptimization.

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

We propose a probabilistic numerical algorithm to solve Backward Stochastic Differential Equations (BSDEs) with nonnegative jumps, a class of BSDEs introduced in [9] for representing fully nonlinear HJB equations. In particular, this allows us to numerically solve stochastic control problems with controlled volatility,…

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic desc…

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

New method calculates Shapley values for uncertain functions.

Uncertainty propagation in nonlinear dynamic systems remains an outstanding problem in scientific computing and control. Numerous approaches have been developed, but are limited in their capability to tackle problems with more than a few uncertain variables or require large amounts of simulation data. In this paper, we…

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…

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…

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 algorithm for uncertain time series classification.

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

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.

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

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

Unified control theory and machine learning for safety in uncertain systems.

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

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.

In this paper, we propose the uncertain volatility models with stochastic bounds. Like the regular uncertain volatility models, we know only that the true model lies in a family of progressively measurable and bounded processes, but instead of using two deterministic bounds, the uncertain volatility fluctuates between …

UnKGCP generates prediction intervals for uncertain knowledge graphs with statistical guarantees.

AIRBO optimizes robustly under uncertain inputs.

Novel pricing method for equity-indexed annuities under uncertain volatility and stochastic interest rate.

GPMI method interpolates uncertain atrial conduction velocity on non-Euclidean manifolds.

A machine learning approach to compute Black-Scholes prices with uncertain volatility.

Algorithm identifies optimal stable matching in uncertain two-sided markets.