Decision Making Under Uncertainty
45 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
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xView3-SAR
Risk-Aware Planning Dataset
Latest papers
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.
Bayesian Optimization with Conformal Prediction Sets
samuelstanton/conformal-bayesopt
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Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.
Calibration tests beyond classification
In the machine learning literature, different measures and statistical tests have been proposed and studied for evaluating the calibration of classification models.
Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem
Bandits with knapsacks (BwK) is an influential model of sequential decision-making under uncertainty that incorporates resource consumption constraints.
jsdp: a Java Stochastic DP Library
Stochastic Dynamic Programming is a branch of Stochastic Programming that takes a "functional equation" approach to the discovery of optimal policies.
Multi-armed Bandit Learning on a Graph
The graph defines the agent's freedom in selecting the next available nodes at each step.
Hindsight Learning for MDPs with Exogenous Inputs
Many resource management problems require sequential decision-making under uncertainty, where the only uncertainty affecting the decision outcomes are exogenous variables outside the control of the decision-maker.
xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagery
diux-xview/xview3-reference
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Unsustainable fishing practices worldwide pose a major threat to marine resources and ecosystems.
Neur2SP: Neural Two-Stage Stochastic Programming
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty.
Deep Reinforcement Learning for Time Allocation and Directional Transmission in Joint Radar-Communication
joleeson/Directional-JRC
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IEEE Wireless Communications and Networking Conference (WCNC) 2022
In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.
