arXiv research

Develops BPDS for better financial portfolio decisions.

Paper proposes a new method to attack Bayesian forecasting models.

Optimizes predictions for specific tasks using parametrized decision analysis.

Bayesian method selects subsets for LMMs with structured dependence.

Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian …

Bayesian method improves quantile estimation and subset selection.

New algorithm reduces regret in sequential decision-making problems.

The paper analyzes and proposes a new stopping criterion for recursive Bayesian classification.

The study analyzes when Bayesian averaging over decision trees is reliable.

Bayesian algorithms perform well even with misspecified priors, especially in meta-learning.

AI-driven Bayesian inference improves decision-making uncertainty.

The paper corrects Bayesian neural network approximations to improve decision quality.

Develops Bayesian approach for end-to-end learning in stochastic optimization.

MINTS uses a minimalist Bayesian framework to tackle multi-armed bandits with structural constraints.

Bayesian framework robustifies classifiers against malicious attacks.

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

In typical applications of Bayesian optimization, minimal assumptions are made about the objective function being optimized. This is true even when researchers have prior information about the shape of the function with respect to one or more argument. We make the case that shape constraints are often appropriate in at…

Unified Bayesian framework for PTA data analysis tackles hierarchical model issues.

Novel approach for estimating conditional expectations using Bayesian quadrature.

Efficient Bayesian decision-making with intractable likelihoods.

Study on Bayesian reinforcement learning performance bounds.

Bayesian principles improve agentic AI decision-making.

We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then... statements (e.g., if high blood pressure, then stroke) that discretize a high-dimensional, multivariate feature space into a series of sim…

New algorithms for fast online decision making using neural networks and martingale posteriors.

This review examines bandit problems in AI using statistical methods.

Paper presents a Bayesian-decision-theory framework for long-tailed classification.

This paper improves Bayesian decision tree learning using HMC.

Develops robust ML systems for predictive uncertainties and adversarial examples.

Bayesian GBMs improve predictive uncertainty calibration for tabular data.

The paper addresses frequentist regret of Linear Thompson Sampling in stochastic linear bandits.

FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.

Bayesian framework for policy learning in decision problems.

Optimizes portfolio construction using Bayesian methods and variational techniques.

Low-dimensional probability models for local distribution functions in a Bayesian network include decision trees, decision graphs, and causal independence models. We describe a new probability model for discrete Bayesian networks, which we call an embedded Bayesian network classifier or EBNC. The model for a node $Y$ g…

Bayesian framework for learning optimal action-value function in MDPs.

We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structur…

Bayesian approach to selecting data for machine learning.

Bayesian models quantify uncertainty and facilitate optimal decision-making in downstream applications. For most models, however, practitioners are forced to use approximate inference techniques that lead to sub-optimal decisions due to incorrect posterior predictive distributions. We present a novel approach that corr…

Large uncertainties in many phenomena have challenged decision making. Collecting additional information to better characterize reducible uncertainties is among decision alternatives. Value of information (VoI) analysis is a mathematical decision framework that quantifies expected potential benefits of new data and ass…

Proposes efficient sensitivity analysis for complex Bayesian models.

qEUBO optimizes decision-making with noisy feedback.

Infinite BART model selects number of trees and allows different functions for clusters.

We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated U…

We consider the problem of how decision making can be fair when the underlying probabilistic model of the world is not known with certainty. We argue that recent notions of fairness in machine learning need to explicitly incorporate parameter uncertainty, hence we introduce the notion of {\em Bayesian fairness} as a su…

Improves DRO with Bayesian Ambiguity Sets for model misspecification.

Neural network approximates Bayesian decision-making parameters.

Diffusion approximations optimize sequential experimentation for uncertain parameters.

GoBOED optimizes experiments for specific decision-making objectives, improving downstream outcomes.