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

16 May 2020Yu-Group/covid19-severity-prediction

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.

173

# A General Framework for Uncertainty Estimation in Deep Learning

16 Jul 2019mattiasegu/uncertainty_estimation_deep_learning

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.

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

27 May 2020sisl/SCoBA.jl

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

14

# Dynamic Real-time Multimodal Routing with Hierarchical Hybrid Planning

5 Feb 2019sisl/DreamrHHP

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

13

# 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 2019grockious/lcrl

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.

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# Certified Reinforcement Learning with Logic Guidance

2 Feb 2019grockious/lcrl

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 2018grockious/lcrl

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

10