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Found 6 papers, 0 papers with code
Content Prompting: Modeling Content Provider Dynamics to Improve User Welfare in Recommender Ecosystems
A prompting policy is a sequence of such prompts that is responsive to the dynamics of a provider's beliefs, skills and incentives.
Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
These guarantees apply to infinite families of cutting planes, such as the family of Gomory mixed integer cuts, which are responsible for the main breakthrough speedups of integer programming solvers.
Improved Sample Complexity Bounds for Branch-and-Cut
If the training set is too small, a configuration may have good performance over the training set but poor performance on future integer programs.
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
We first bound the sample complexity of learning cutting planes from the canonical family of Chv\'atal-Gomory cuts.
Incentive Compatible Active Learning
We consider active learning under incentive compatibility constraints.
Learning Time Dependent Choice
We find that the commonly studied stream-based setting is in general difficult to analyze for preference models, but we provide a redeeming situation in which the learner can indeed improve upon the guarantees provided by PAC learning.
