# Decision Making Under Uncertainty   Edit

21 papers with code • 0 benchmarks • 1 datasets

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# Curating a COVID-19 data repository and forecasting county-level death counts in the United States

16 May 2020

We use this data to develop predictions and corresponding prediction intervals for the short-term trajectory of COVID-19 cumulative death counts at the county-level in the United States up to two weeks ahead.

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# A General Framework for Uncertainty Estimation in Deep Learning

16 Jul 2019

Current approaches for uncertainty estimation of neural networks require changes to the network and optimization process, typically ignore prior knowledge about the data, and tend to make over-simplifying assumptions which underestimate uncertainty.

154

# Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints

27 May 2020

We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty.

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# Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification

4 Dec 2020

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.

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# Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

5 Feb 2019

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

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# A Probabilistic Model of the Bitcoin Blockchain

7 Nov 2018

The Bitcoin transaction graph is a public data structure organized as transactions between addresses, each associated with a logical entity.

11

# 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 $W$.

10

# Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees

11 Sep 2019

Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology.

10

# Certified Reinforcement Learning with Logic Guidance

2 Feb 2019

This probability (certificate) is also calculated in parallel with policy learning when the state space of the MDP is finite: as such, the RL algorithm produces a policy that is certified with respect to the property.

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# Logically-Constrained Reinforcement Learning

24 Jan 2018

With this reward function, the policy synthesis procedure is "constrained" by the given specification.

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