A novel decoupled method for distributed saddle problems achieves optimal communication complexity via multi-stage residual norm minimization, with a matching lower bound and extension to variational inequalities.
Econometrica: Journal of the Econometric Society , pages=
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Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
A Nash equilibrium framework for training-free multimodal step verification that uses cross-modal agreement and disagreement signals for filtering and ranking reasoning steps.
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Efficient Gradient Methods for Distributed Saddle Problems
A novel decoupled method for distributed saddle problems achieves optimal communication complexity via multi-stage residual norm minimization, with a matching lower bound and extension to variational inequalities.
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Principal-agent problems with adverse selection: A stochastic target problem formulation
Agent's optimization in unique-contract principal-agent problem with adverse selection is recast as stochastic target problem, enabling principal's objective as stochastic optimal control with partial information and state constraints.
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Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
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A Nash Equilibrium Framework For Training-Free Multimodal Step Verification
A Nash equilibrium framework for training-free multimodal step verification that uses cross-modal agreement and disagreement signals for filtering and ranking reasoning steps.