366,329 research outputs found
Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions
Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades these guarantees. In this paper, we seek to unify techniques from control theory and machine learning to synthesize controllers that achieve safety in the presence of measurement model uncertainty. We define the notion of a Measurement-Robust Control Barrier Function (MR-CBF) as a tool for determining safe control inputs when facing measurement model uncertainty. Furthermore, MR-CBFs are used to inform sampling methodologies for learning-based perception systems and quantify tolerable error in the resulting learned models. We demonstrate the efficacy of MR-CBFs in achieving safety with measurement model uncertainty on a simulated Segway system
Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
Prescriptive process monitoring methods seek to optimize the performance of
business processes by triggering interventions at runtime, thereby increasing
the probability of positive case outcomes. These interventions are triggered
according to an intervention policy. Reinforcement learning has been put
forward as an approach to learning intervention policies through trial and
error. Existing approaches in this space assume that the number of resources
available to perform interventions in a process is unlimited, an unrealistic
assumption in practice. This paper argues that, in the presence of resource
constraints, a key dilemma in the field of prescriptive process monitoring is
to trigger interventions based not only on predictions of their necessity,
timeliness, or effect but also on the uncertainty of these predictions and the
level of resource utilization. Indeed, committing scarce resources to an
intervention when the necessity or effects of this intervention are highly
uncertain may intuitively lead to suboptimal intervention effects. Accordingly,
the paper proposes a reinforcement learning approach for prescriptive process
monitoring that leverages conformal prediction techniques to consider the
uncertainty of the predictions upon which an intervention decision is based. An
evaluation using real-life datasets demonstrates that explicitly modeling
uncertainty using conformal predictions helps reinforcement learning agents
converge towards policies with higher net intervention gai
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
This work takes a critical look at the application of conventional machine
learning methods to wireless communication problems through the lens of
reliability and robustness. Deep learning techniques adopt a frequentist
framework, and are known to provide poorly calibrated decisions that do not
reproduce the true uncertainty caused by limitations in the size of the
training data. Bayesian learning, while in principle capable of addressing this
shortcoming, is in practice impaired by model misspecification and by the
presence of outliers. Both problems are pervasive in wireless communication
settings, in which the capacity of machine learning models is subject to
resource constraints and training data is affected by noise and interference.
In this context, we explore the application of the framework of robust Bayesian
learning. After a tutorial-style introduction to robust Bayesian learning, we
showcase the merits of robust Bayesian learning on several important wireless
communication problems in terms of accuracy, calibration, and robustness to
outliers and misspecification.Comment: Submitted for publicatio
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