Proposes OPAC for trajectory-level offline RL achieving 𝓣O(H^{2}√(C_sa(π*)/n)) bounds with matching lower bound, plus conditions for tractability in generalized nonlinear outcome settings.
Efficient GPU memory management for large model inference in cloud containers
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ADAPT uses an EWMA estimator for cold-start durations to set a dynamic horizon in an MPC-based proactive autoscaler, achieving under 5% SLA violations with MPC+LSTM across tested workloads versus higher rates for HPA and MPC+Prophet.
citing papers explorer
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When Does Trajectory-Level Supervision Permit Efficient Offline Reinforcement Learning?
Proposes OPAC for trajectory-level offline RL achieving 𝓣O(H^{2}√(C_sa(π*)/n)) bounds with matching lower bound, plus conditions for tractability in generalized nonlinear outcome settings.
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ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestration
ADAPT uses an EWMA estimator for cold-start durations to set a dynamic horizon in an MPC-based proactive autoscaler, achieving under 5% SLA violations with MPC+LSTM across tested workloads versus higher rates for HPA and MPC+Prophet.