Decision Making Under Uncertainty
43 papers with code • 0 benchmarks • 2 datasets
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Libraries
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xView3-SAR
Risk-Aware Planning Dataset
Latest papers
High-dimensional forecasting with known knowns and known unknowns
Forecasts play a central role in decision making under uncertainty.
Decision Making in Non-Stationary Environments with Policy-Augmented Search
In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment.
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
facebookresearch/infoshap
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NeurIPS 2023
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models.
Measurement Simplification in ρ-POMDP with Performance Guarantees
In both cases we show a significant speed-up in planning with performance guarantees.
Simple Modification of the Upper Confidence Bound Algorithm by Generalized Weighted Averages
In preliminary experiments, we investigated the optimal parameters of a simple generalized UCB1 (G-UCB1), prepared for comparison and GWA-UCB1, in a stochastic MAB problem with two arms.
Uncertainty Quantification for Image-based Traffic Prediction across Cities
alextimans/traffic4cast-uncertainty
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We compare two epistemic and two aleatoric UQ methods on both temporal and spatio-temporal transfer tasks, and find that meaningful uncertainty estimates can be recovered.
Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers
vaishwarya96/maple-uncertainty-estimation
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Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-of-distribution (OOD) samples.
Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
rothkopflab/nioc-neurips
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NeurIPS 2023
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
Minimax-Bayes Reinforcement Learning
While the Bayesian decision-theoretic framework offers an elegant solution to the problem of decision making under uncertainty, one question is how to appropriately select the prior distribution.
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm
We consider the problem of decision-making under uncertainty in an environment with safety constraints.
