The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
A novel framework approximates unbalanced optimal transport using Neural ODEs via a generalized discrete problem, a Sinkhorn-inspired scheme with proven convergence and error estimates, and derived transport dynamics.
Increased regularization is required for group DRO to achieve good worst-group generalization in overparameterized neural networks.
citing papers explorer
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Taming the Curses of Multiagency in Robust Markov Games with Large State Space through Linear Function Approximation
The work gives the first algorithms for general robust Markov games with linear function approximation whose sample complexity breaks the curse of multiagency for large state spaces in both generative and online settings.
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Toward Calibrated, Fair, and accurate Deepfake Detection
Face-Feature Tuning is a label-free logit remapping method that reduces FPR/TPR gaps across groups in deepfake detection while preserving overall accuracy.
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Control, Optimal Transport and Neural Differential Equations in Supervised Learning
A novel framework approximates unbalanced optimal transport using Neural ODEs via a generalized discrete problem, a Sinkhorn-inspired scheme with proven convergence and error estimates, and derived transport dynamics.
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Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization
Increased regularization is required for group DRO to achieve good worst-group generalization in overparameterized neural networks.