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Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:

Koul, Anurag

Abstract:

Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...

Resource Type:

Dissertation
Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:

Dodge, Jonathan E.

Abstract:

Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...

Resource Type:

Dissertation

Full Text:

taught me that learning is a great pastime; Andrew Anderson, my colleague in PhD trials and tribulations
Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Creator:

Zemicheal, Tadesse H.

Abstract:

Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...

Resource Type:

Dissertation