arXiv research

Framework for quantifying uncertainty in dynamic processes.

Uncertainty modeling for dynamical systems

Bayesian method identifies dynamical models with uncertainty quantification.

A new framework for robust policy learning in MDPs with linear mixture dynamics.

Study evaluates machine learning methods for uncertainty quantification in complex systems.

Paper recovers uncertainty from dynamic valuation rules.

Study designs steering rewards for MFGs with unknown dynamics and model uncertainty.

We consider dynamic sublinear expectations (i.e., time-consistent coherent risk measures) whose scenario sets consist of singular measures corresponding to a general form of volatility uncertainty. We derive a càdlàg nonlinear martingale which is also the value process of a superhedging problem. The superhedging strate…

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingl…

The paper models insurance market dynamics under uncertainty and financial frictions.

New method combines ODE filters and numerical quadrature to propagate model uncertainty.

New method uses biased MD to create accurate MLIPs.

New method calibrates predictions in chaotic systems using variational inference.

New algorithm minimizes worst-case regret in uncertain, time-varying dynamics.

Valid prediction sets for dynamic graphs using conformal prediction.

This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter es…

Study vector-valued robust control under uncertainty.

Unified deep sequential and state-space models for robust option pricing with uncertainty.

Framework for robust control under model uncertainty, improving financial derivatives hedging.

Framework for optimizing portfolios under model uncertainty.

Improves model accuracy for neural nets in stochastic dynamics with partial prior knowledge.

Adaptive AI delegation framework for dynamic decision authority allocation.

Nonlocal Bayesian modeling for continuous spatio-temporal dynamics

ConfEviSurrogate improves surrogate model accuracy and uncertainty quantification.

Surrogate models help predict complex systems with less computational cost.

Paper proposes a new framework for robust multi-modal data fusion under uncertainty.

New method distinguishes data noise from GP uncertainty.

SDE-Net quantifies uncertainty in deep nets using stochastic dynamics.

RRPI improves offline RL by optimizing policies against worst-case dynamics.

New method improves uncertainty quantification for large batch sizes and misspecified models.

Robust Markov Decision Processes (RMDPs) intend to ensure robustness with respect to changing or adversarial system behavior. In this framework, transitions are modeled as arbitrary elements of a known and properly structured uncertainty set and a robust optimal policy can be derived under the worst-case scenario. In t…

We consider fundamental questions of arbitrage pricing arising when the uncertainty model is given by a set of possible mutually singular probability measures. With a single probability model, essential equivalence between the absence of arbitrage and the existence of an equivalent martingale measure is a folk theorem,…

Study uncovers uncertainty in traffic prediction models across cities.

TULiP estimates uncertainty for deep learning models safely.

We study time consistent dynamic pricing mechanisms of European contingent claims under uncertainty by using G framework introduced by Peng ([24]). We consider a financial market consisting of a riskless asset and a risky stock with price process modelled by a geometric generalized G-Brownian motion, which features the…

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

This work presents the concept of kernel mean embedding and kernel probabilistic programming in the context of stochastic systems. We propose formulations to represent, compare, and propagate uncertainties for fairly general stochastic dynamics in a distribution-free manner. The new tools enjoy sound theory rooted in f…

A new framework reduces inconsistencies in chaotic surrogate modeling.

BSG learns dynamic network spillovers and uncertainty quantification.

We propose a mathematical model for the word-of-mouth communications among stock investors through social networks and explore how the changes of the investors' social networks influence the stock price dynamics and vice versa. An investor is modeled as a Gaussian fuzzy set (a fuzzy opinion) with the center and standar…

DUA-D2C refines D2C for better deep learning generalization.

We develop a tractable and flexible approach for incorporating side information into dynamic optimization under uncertainty. The proposed framework uses predictive machine learning methods (such as $k$-nearest neighbors, kernel regression, and random forests) to weight the relative importance of various data-driven unc…

The study shows how trade uncertainty affects stock-bond correlations over time.

Paper develops a robust hedging framework to reduce market risk and uncertainty.

We develop a robust RL algorithm for off-dynamics environments with improved suboptimality bounds and computational efficiency.

Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. Especially in autonomous systems, wrong predictions for OOD inputs can cause safety critical situations. As a first step towards a solution, we consider the problem of detecting such data in a value-based de…

New RL algorithm learns robust policies without knowing nominal model.

Bayesian deep learning improves seismic imaging uncertainty.