  We address the challenge of learning safe and robust decision policies in
presence of uncertainty in context of the real scientific problem of adaptive
resource oversubscription to enhance resource efficiency while ensuring safety
against resource congestion risk.
  Traditional supervised prediction or forecasting models are ineffective in
learning adaptive policies whereas standard online optimization or
reinforcement learning is difficult to deploy on real systems. Offline methods
such as imitation learning (IL) are ideal since we can directly leverage
historical resource usage telemetry. But, the underlying aleatoric uncertainty
in such telemetry is a critical bottleneck.
  We solve this with our proposed novel chance-constrained imitation learning
framework, which ensures implicit safety against uncertainty in a principled
manner via a combination of stochastic (chance) constraints on resource
congestion risk and ensemble value functions. This leads to substantial
($\approx 3-4\times$) improvement in resource efficiency and safety in many
oversubscription scenarios, including resource management in cloud services.
