Risk-averse Learning with Non-Stationary Distributions

arXiv:2404.02988v1 Announce Type: cross
Abstract: Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due …

abstract adapt arxiv cases change cs.lg cs.sy decision eess.sy environments impact maker negative optimization paper performance risk strategy type