arXiv research

Improves DRO with Bayesian Ambiguity Sets for model misspecification.

New method optimises worst-case risk under model uncertainty.

Robust MDPs (RMDPs) can be used to compute policies with provable worst-case guarantees in reinforcement learning. The quality and robustness of an RMDP solution are determined by the ambiguity set---the set of plausible transition probabilities---which is usually constructed as a multi-dimensional confidence region. E…

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…

Bayesian convolutional deep sets improve ambiguity in stationary process modeling.

Study nonconcave portfolio choice with smooth ambiguity and Bayesian learning.

New method improves robustness of Bayesian experimental design.

New method calibrates ambiguity sets for robust decision-making under contamination.

We address the problem of computing reliable policies in reinforcement learning problems with limited data. In particular, we compute policies that achieve good returns with high confidence when deployed. This objective, known as the \emph{percentile criterion}, can be optimized using Robust MDPs~(RMDPs). RMDPs general…

Unified framework for DRO and DTA using Bayesian nonparametrics.

Bayesian approach to portfolio selection reduces pessimism in frequent trading.

Bayesian EnKF improves sentence comprehension uncertainty modeling.

EDRBO optimizes Bayesian optimization with continuous contexts using ensemble models and robust methods.

Bayes-TrEx finds in-distribution examples for model inspection.

Since exchange economy considerably varies in the market assets, asset prices have become an attractive research area for investigating and modeling ambiguous and uncertain information in today markets. This paper proposes a new generative uncertainty mechanism based on the Bayesian Inference and Correntropy (BIC) tech…

Bayesian Neural Networks improve uncertainty modeling in facial emotion recognition.

Factor analysis aims to determine latent factors, or traits, which summarize a given data set. Inter-battery factor analysis extends this notion to multiple views of the data. In this paper we show how a nonlinear, nonparametric version of these models can be recovered through the Gaussian process latent variable model…

Optimal policies in Markov decision processes (MDPs) are very sensitive to model misspecification. This raises serious concerns about deploying them in high-stake domains. Robust MDPs (RMDP) provide a promising framework to mitigate vulnerabilities by computing policies with worst-case guarantees in reinforcement learn…

Bayesian classifier improves robustness with optimistic score ratio.

This paper compares different DRO formulations for pension fund management.

Bayesian optimization tackles uncertainty in context variables.

Paper investigates Lambda Value-at-Risk under ambiguity and risk sharing.

The paper proves probabilistic alignment between unseen modalities using contrastive learning.

A novel Laplace-approximated Bayesian Tensor Network Kernel Machine (LA-TNKM) provides principled uncertainty estimates.

Adapts AUM to identify ambiguous tasks in crowdsourced learning, improving generalization.

CADRO optimizes DRO by reducing conservatism through cost-aware ambiguity sets.

Proposes a method to learn adaptive ambiguity sets for robust optimization.

The paper uses EVT to improve tail risk measures under ambiguity sets.

Bayesian nonparametrics improves data-driven risk optimization under distributional uncertainty.

According to conventional wisdom, ambiguity accelerates optimal timing by decreasing the value of waiting in comparison with the unambiguous benchmark case. We study this mechanism in a multidimensional setting and show that in a multifactor model ambiguity does not only influence the rate at which the underlying proce…

Model cash management under ambiguity using maxmin preferences and diffusion.

We propose a novel, theoretically-grounded, acquisition function for Batch Bayesian optimization informed by insights from distributionally ambiguous optimization. Our acquisition function is a lower bound on the well-known Expected Improvement function, which requires evaluation of a Gaussian Expectation over a multiv…

Meta-agent learns effective exploration from offline data.

Flexible framework integrates machine learning and DRO for uncertain parameter prediction.

In most classification tasks there are observations that are ambiguous and therefore difficult to correctly label. Set-valued classifiers output sets of plausible labels rather than a single label, thereby giving a more appropriate and informative treatment to the labeling of ambiguous instances. We introduce a framewo…

Study optimal timing to divest from assets with uncertain future scenarios.

Study examines insurance demand under ambiguity aversion.

In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear(in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we i…

DRO optimizes decisions under uncertain distributions, considering worst-case scenarios.

New method for finding optimal treatment regimes in medical settings with time-varying unobserved factors.

Construction of ambiguity set in robust optimization relies on the choice of divergences between probability distributions. In distribution learning, choosing appropriate probability distributions based on observed data is critical for approximating the true distribution. To improve the performance of machine learning …

We introduce a measure to quantify ambiguity in deep learning models, improving their reliability.

Model-free preference under ambiguity defined and applied.

This paper compares average-K and top-K classification methods under ambiguity.

This paper formulates a model of utility for a continuous time framework that captures the decision-maker's concern with ambiguity about both the drift and volatility of the driving process. At a technical level, the analysis requires a significant departure from existing continuous time modeling because it cannot be d…

Study finds cheapest possible payoff under ambiguity, linking to maxmin expected utility.

This paper improves conformal prediction for robust interval estimation under distribution shifts.

Bayesian method improves deep learning for noisy EEG seizure detection.