Bayesian principles improve agentic AI decision-making.
problem Decision-making under uncertainty in agentic AI systems.
method Bayesian decision theory applied to the orchestration layer of agentic AI.
result Bayesian principles enhance agentic AI's ability to make decisions under uncertainty.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
Adaptive AI delegation framework for dynamic decision authority allocation.
problem Dynamic allocation of decision authority to AI-generated recommendations under evolving evidence quality and uncertainty.
method Formulated as a Governance-Aware POMDP, using Bayesian inference for informational state estimation and sequential optimization for authority allocation.
result Sequential Bayesian governance provides the strongest general-purpose policy across AI-quality regimes, adapting to evolving evidence.
AI-driven Bayesian inference improves decision-making uncertainty.
problem Lack of certainty in AI predictions.
method Non-parametric Bayesian framework with Dirichlet process prior and AI-driven baseline.
result AI predictions can be integrated into Bayesian analysis for predictive inference and uncertainty quantification.
Develops a framework for quantifying agentic AI model risk using LLM-inferred Bayesian state filters.
problem Quantifying the risk of agentic AI systems due to uncertain beliefs and actions.
method Representing the system as a partially observed Markov decision process with latent states, Bayesian belief updates, control-dependent losses, and tail-risk functionals.
result Develops a rigorous framework for separating uncertainty quantification from risk measurement.
Improves AI-prior reliability for Bayesian inference.
problem Error propagation from predictive models into posterior inference.
method Rectified AI-informed prior elicitation framework.
result Significant reduction in bias and improvement in predictive performance.
Bayesian reflex models AI learning like the autonomic nervous system.
problem Online learning in dynamic AI environments.
method Bayesian online algorithms with belief maintenance, sequential updating, and uncertainty-driven action balancing.
result Unified framework for adaptive AI learning.
Bayesian framework uses AI-generated data to improve parameter estimation.
problem Parameter estimation in models with unknown or unspecified likelihood.
method Exponentially tilted empirical likelihood with Dirichlet process posterior.
result AI-generated data can provide useful regularization for parameter estimation.
MissBGM uses AI and Bayesian modeling for better missing data imputation.
problem Missing data imputation in data science, especially with uncertainty quantification.
method AI-powered Bayesian generative modeling with explicit modeling of missingness mechanisms.
result MissBGM provides principled posterior uncertainty over imputations and superior performance.
Adaptive querying learns user psychometrics with AI personas.
problem Learning user psychometrics within query budgets.
method Persona-induced latent variable model with AI personas and large language model response distributions.
result Persona-based posteriors deliver accurate probabilistic predictions.
Unified framework for arbitrary conditional inference using AI and Bayesian methods.
problem Limited flexibility in existing conditional inference methods.
method Bayesian generative modeling with stochastic iterative algorithm.
result Single learned model for universal conditional prediction with uncertainty quantification.
CausalBGM uses AI to infer causal effects from complex data.
problem Challenges in causal inference with high-dimensional covariates.
method AI-powered Bayesian generative modeling approach to estimate individual treatment effects.
result CausalBGM consistently outperforms existing methods in high-dimensional scenarios.
AIS algorithm improves heavy-tailed distribution estimation.
problem Inconsistent estimators and slow convergence in AIS for heavy-tailed distributions.
method Adapts Student-t proposal distributions by matching escort moments and minimizing α-divergence.
result Improves estimation accuracy for heavy-tailed distributions.
CoExBO optimizes lithium-ion batteries with user input, enhancing trust and efficiency.
problem User distrust in Bayesian optimization due to opacity and lack of user input.
method Preference learning and iterative explanation to integrate user insights.
result Algorithm converges to optimal solution even with adversarial user inputs.
Bayesian test assesses dependence between mixed data types.
problem Assessing dependence between text, image, and sound data.
method Bayesian kernelised correlation test using Dirichlet process model.
result Demonstrated effectiveness compared to other methods.
This paper introduces Probability Engineering to improve deep learning models.
problem Challenges in traditional probabilistic modeling for AI applications.
method Treats learned probability distributions as engineering artifacts and actively modifies them.
result Improves robustness, efficiency, adaptability, and trustworthiness of deep learning models.
Paper proposes government indemnification for AI risks to solve judgment-proof problem.
problem Uninsurable risks from AI, especially existential risks, create a judgment-proof problem.
method A government-provided, mandatory indemnification program using risk-priced fees and Bayesian Truth Serum.
result The approach better leverages private information and signals risk mitigation efforts.
The paper proposes AIS for Bayesian inversion of multioutput signals with covariance estimation.
problem Performing uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals.
method Adaptive Importance Sampling (AIS) scheme, split variables, frequentist approach for noise covariance, prior density over covariance matrix.
result Estimation of model parameters and covariance matrix of noise.
Bayesian deep learning improves deep learning's capabilities across diverse settings.
problem Overlooked metrics, tasks, and data types in deep learning.
method Revisits strengths of Bayesian deep learning and addresses challenges.
result Bayesian deep learning can elevate deep learning's capabilities across diverse settings.
New method uses Rashomon sets to improve Bayesian inference in factorial designs.
problem Combustion of model uncertainty in factorial designs leads to multimodal posterior and convergence issues.
method Rashomon-seeded annealing, integrating high-performing models as warm start for AIS.
result Restores full posterior inference without exhaustive enumeration of model space.
Bayesian PROCOVA uses AI to adjust for covariates in RCTs.
problem Unbiased and precise treatment effect inferences from RCTs.
method Generative AI constructs digital twins for covariate adjustment, using an additive mixture prior.
result Efficiency gains in smaller RCTs compared to frequentist methods.
RestoreAI predicts landmine risk from patterns, improving clearance efficiency.
problem Predicting landmine risk from spatial patterns to enhance clearance efficiency.
method RestoreAI uses landmine patterns for risk prediction, implementing three deminers: linear, curved, and Bayesian.
result RestoreAI significantly boosts clearance efficiency, achieving a 14.37 percentage point increase in cleared landmines per timestep.
Delta-AI speeds up inference in sparse PGMs by local credit assignment.
problem Efficient inference in sparse probabilistic graphical models.
method Local credit assignment in agent's policy learning objective.
result Trained sampler recovers marginals and conditional distributions.
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
problem Resource-intensive evaluation of generative AI models.
method ProEval uses pre-trained Gaussian Processes and Bayesian quadrature to estimate performance and discover failures.
result ProEval requires significantly fewer samples to achieve accurate performance estimates and reveals more diverse failure cases.
Enhances BO with expert preferences about abstract properties.
problem Lack of expert knowledge in BO for black-box experimental design.
method Human-AI collaboration to incorporate expert preferences into surrogate modeling.
result Superior performance compared to baselines in synthetic and real-world datasets.
New algorithms improve sampling from Bayesian deep learning models.
problem Sampling from the posterior of deep neural networks is inefficient.
method Adaptive SGMCMC algorithms with biased drift.
result Proposed algorithms significantly outperform existing methods.
Uncertainty estimation in deep neural networks is essential for designing reliable and robust AI systems. Applications such as video surveillance for identifying suspicious activities are designed with deep neural networks (DNNs), but DNNs do not provide uncertainty estimates. Capturing reliable uncertainty estimates i…
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
New framework learns complex AI attitudes from heterogeneous data.
problem Heterogeneous ordinal structure in AI attitudes, poorly captured by existing methods.
method Monotone Gaussian score embedding, BNP complexity discovery, confirmatory fixed-K estimation.
result Reduced holdout MSE by 25.8% over single-graph baseline.
This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are dev…
Bayesian Neural ODEs improve vessel trajectory prediction with better uncertainty estimates.
problem Challenges in predicting vessel trajectories from irregular AIS data.
method Adopted a Gaussian process (GP) kernel-based prior on the vector field evaluated at measurement points, combined with probabilistic multiple shooting for long trajectories.
result Improved accuracy and uncertainty quantification in vessel trajectory predictions.
AI helps HEP measure uncertainties, but needs better interpretation.
problem Uncertainty interpretation in AI for HEP measurements.
method Discussing existing AI methods for inference, simulation, and control/decision-making.
result Need for trustworthy AI UQ methods for widespread usage.
The paper uses XAI to predict RFQ fulfillment accuracy.
problem Improving accuracy in predicting RFQ fulfillment for less liquid asset classes.
method Advanced algorithms like Logistic Regression, Random Forest, XGBoost, and Bayesian Neural Tree.
result Improved accuracy in RFQ fill rate predictions.
Extends local attributions to Bayesian Neural Networks for improved explanations.
problem Lack of explanations for Bayesian Neural Networks' predictions.
method Extend local attributions to a probabilistic explanation distribution of BNNs.
result Enriches standard explanations with uncertainty information and visualizes explanation stability.
ShapleyBO explains BO's decisions, enhancing human-AI collaboration in robotics.
problem Lack of explainability in BO's black box nature, especially in human-in-the-loop applications.
method Uses game-theoretic Shapley values to quantify and interpret BO's parameter contributions.
result ShapleyBO identifies exploration and exploitation contributions, disentangling aleatoric and epistemic uncertainty.
An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability …
New algorithm improves Bayesian neural networks using adaptive importance sampling.
problem High computational cost in training Bayesian neural networks.
method Adaptive Importance Sampling (AIS) integrated into a novel algorithm (PMCnet).
result Improved performance and exploration capabilities for both shallow and deep neural networks.
This review examines bandit problems in AI using statistical methods.
problem Sequential decision-making under uncertainty in AI environments.
method Foundational models, concentration inequalities, minimax regret bounds, frequentist and Bayesian algorithms, K-armed contextual bandits, SCAB, functional data analysis.
result Exploration-exploitation trade-offs and regret analyses in various bandit problems.
This paper develops a stochastic learning-optimization model for resilient automotive supply chains.
problem Supply chain disruptions and volatile demand pose challenges to the UK automotive industry.
method Integrates Bayesian inference with inventory optimization for a two-echelon system subject to stochastic demand and disruptions.
result The integrated approach achieves significant cost reductions and improved resilience during disruptions.
DAIS improves AIS for differentiable marginal likelihood estimation.
problem Differentiable marginal likelihood estimation for complex models.
method Proposes Differentiable Annealed Importance Sampling (DAIS) to make AIS differentiable.
result DAIS achieves convergence and consistency in Bayesian linear regression.
Deep learning excels in AI but struggles with causal physics.
problem Deep learning struggles with causal relationships in physical sciences.
method Combining Bayesian methods, physical constraints, and causal models.
result Deep learning can mislead in systems with unclear causal relationships.
New approach improves AI's handling of incomplete data.
problem Improving AI's ability to work with incomplete data.
method Proposes a new likelihood-free EM algorithm for faster, more efficient inference.
result More statistically efficient than masking approach and faster than conventional EM.
Bayesian Neural Networks improve OOD detection with limited data.
problem Limited training data hinders reliable OOD detection.
method Bayesian Neural Networks with expected logit vectors.
result Bayesian methods outperform deterministic methods in small data settings.
AIF improves physical AI agents' performance in dynamic environments.
problem Physical AI agents are less capable than biological agents in open-ended real-world environments.
method Developed from probability theory, Bayesian machine learning, variational inference, and Active Inference (AIF), grounded in the Free Energy Principle.
result AIF minimizes variational free energy and is well-suited to physical constraints.
New attack targets speech-based AI models via stock market data.
problem Vulnerabilities in speech-based AI models using LLMs.
method Backdoor attack using acoustic data poisoning.
result Shows possible vulnerabilities in speech-based transformers.
AI-driven framework optimizes MCMC-based preconditioners for faster linear system solving.
problem Slow convergence of Krylov subspace solvers for ill-conditioned matrices.
method Graph neural surrogate and Bayesian optimization for AI-tuned MCMC parameters.
result 50% reduction in iterations to convergence on unseen system.
The book covers scalable MCMC methods for Bayesian learning.
problem Scalability issues in Bayesian learning with large datasets.
method Advanced MCMC algorithms, including stochastic gradient, non-reversible, and continuous time methods.
result Substantial advances in practical and theoretical Bayesian computation.
SmallML predicts customer churn for SMEs with small data, improving accuracy by 24.2 points.
problem AI exclusion of SMEs due to data scale mismatch.
method Bayesian transfer learning with hierarchical pooling and conformal prediction.
result 96.7% AUC on 100 obs SMEs, 24.2 point improvement over logistic regression.