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Distributionally Time-Varying Online Stochastic Optimization under Polyak-Łojasiewicz Condition with Application in Conditional Value-at-Risk Statistical Learning
In this work, we consider a sequence of stochastic optimization problems following a time-varying distribution via the lens of online optimization.
Distributionally Robust Graph Learning from Smooth Signals under Moment Uncertainty
To address this issue, we propose a novel graph learning model based on the distributionally robust optimization methodology, which aims to identify a graph that not only provides a smooth representation of but is also robust against uncertainties in the observed signals.
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