Exponentially-shifted Gaussian smoothing yields zeroth-order gradient estimators with linear dimension dependence, enabling improved complexity bounds for stochastic optimization including decision-dependent regimes.
Outside the echo chamber: Optimizing the performative risk
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The paper proposes Decoupled Performative Prediction showing that institutions achieve lower risk by using distinct internal decision models and disclosed models, with an algorithm that converges under standard assumptions and a deception cost metric that self-imposed constraints do not sufficiently
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Complexity Guarantees for Zeroth-order Methods via Exponentially-shifted Gaussian Smoothing: Mitigating Dimension-dependence and Incorporating Decision-dependence
Exponentially-shifted Gaussian smoothing yields zeroth-order gradient estimators with linear dimension dependence, enabling improved complexity bounds for stochastic optimization including decision-dependent regimes.
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Strategically Deceptive Model Deployment in Performative Prediction
The paper proposes Decoupled Performative Prediction showing that institutions achieve lower risk by using distinct internal decision models and disclosed models, with an algorithm that converges under standard assumptions and a deception cost metric that self-imposed constraints do not sufficiently