# Decision Making Under Uncertainty

37 papers with code • 0 benchmarks • 2 datasets

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# Bayesian Optimization of Risk Measures

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 $W$.

# 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

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

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

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.

# Certified Reinforcement Learning with Logic Guidance

Reinforcement Learning (RL) is a widely employed machine learning architecture that has been applied to a variety of control problems.

# Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

We introduce the problem of Dynamic Real-time Multimodal Routing (DREAMR), which requires planning and executing routes under uncertainty for an autonomous agent.