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Found 5 papers, 3 papers with code
Filtered Direct Preference Optimization
This paper addresses the issue of text quality within the preference dataset by focusing on Direct Preference Optimization (DPO), an increasingly adopted reward-model-free RLHF method.
Slingshot Perturbation to Learning in Monotone Games
This paper addresses the problem of learning Nash equilibria in {\it monotone games} where the gradient of the payoff functions is monotone in the strategy profile space, potentially containing additive noise.
Last-Iterate Convergence with Full and Noisy Feedback in Two-Player Zero-Sum Games
This paper proposes Mutation-Driven Multiplicative Weights Update (M2WU) for learning an equilibrium in two-player zero-sum normal-form games and proves that it exhibits the last-iterate convergence property in both full and noisy feedback settings.
Mutation-Driven Follow the Regularized Leader for Last-Iterate Convergence in Zero-Sum Games
In this study, we consider a variant of the Follow the Regularized Leader (FTRL) dynamics in two-player zero-sum games.
Automated Segmentation of Hip and Thigh Muscles in Metal Artifact-Contaminated CT using Convolutional Neural Network-Enhanced Normalized Metal Artifact Reduction
Our goal was to develop an automated segmentation method of the bones and muscles in the postoperative CT images.
