Decision Making Under Uncertainty
44 papers with code • 0 benchmarks • 2 datasets
Benchmarks
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Libraries
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xView3-SAR
Risk-Aware Planning Dataset
Most implemented papers
Neur2SP: Neural Two-Stage Stochastic Programming
Stochastic Programming is a powerful modeling framework for decision-making under uncertainty.
Bayesian Optimization of Risk Measures
saitcakmak/BoRisk
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NeurIPS 2020
We consider Bayesian optimization of objective functions of the form $\rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function and $\rho$ denotes either the VaR or CVaR risk measure, computed with respect to the randomness induced by the environmental random variable $W$.
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
ENSTA-U2IS-AI/torch-uncertainty
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Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks.
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
js05212/PyTorch-for-NPN
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NeurIPS 2016
Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models.
Logically-Constrained Reinforcement Learning
With this reward function, the policy synthesis procedure is "constrained" by the given specification.
Calibrating Deep Convolutional Gaussian Processes
GiaLacTRAN/convolutional_deep_gp_random_features
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The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.
Probabilistic Logic Programming with Beta-Distributed Random Variables
We enable aProbLog---a probabilistic logical programming approach---to reason in presence of uncertain probabilities represented as Beta-distributed random variables.
Factorized Machine Self-Confidence for Decision-Making Agents
COHRINT/FaMSeC
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Markov decision processes underlie much of the theory of reinforcement learning, and are commonly used for planning and decision making under uncertainty in robotics and autonomous systems.
A Probabilistic Model of the Bitcoin Blockchain
The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity.
