Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Introduces IFSC framework modeling peer imitation in individual fairness-aware strategic classification to improve fairness consistency under interdependent manipulations.
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
Private variants of individual and unit-level aid allocation admit interpretable bounds trading privacy, efficiency, and targeting precision in stochastic and distribution-free settings.
Error-rate balance and predictive parity become compatible under endogenous behavior by adjusting stakes differently across groups, introducing a new form of unequal treatment in consequences.
Introduces partial fairness awareness (PFA) and a belief-guided mechanism allowing strategic agents to align beliefs with a hidden grounding fairness constraint via iterative interaction.
citing papers explorer
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Nearly-Optimal Bandit Learning in Stackelberg Games with Side Information
Algorithms achieve O(T^{1/2}) regret in contextual Stackelberg games via reduction to linear contextual bandits, improving on prior O(T^{2/3}) rates.
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Beyond Independent Manipulation: Individual Fairness-aware Strategic Classification with Peer Imitation
Introduces IFSC framework modeling peer imitation in individual fairness-aware strategic classification to improve fairness consistency under interdependent manipulations.
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When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
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Privacy, Prediction, and Allocation
Private variants of individual and unit-level aid allocation admit interpretable bounds trading privacy, efficiency, and targeting precision in stochastic and distribution-free settings.
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Revisiting Fairness Impossibility with Endogenous Behavior
Error-rate balance and predictive parity become compatible under endogenous behavior by adjusting stakes differently across groups, introducing a new form of unequal treatment in consequences.
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Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
Introduces partial fairness awareness (PFA) and a belief-guided mechanism allowing strategic agents to align beliefs with a hidden grounding fairness constraint via iterative interaction.