Optimization
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Convexity and Lipschitz Hessian: Minimax Sample Complexity
Neural Information Processing Systems
We provide the first tight characterization for the rate of the minimax simple regret by developing matching upper and lower bounds.
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Scalable Kernel Inverse Optimization
Neural Information Processing Systems
The inherent assumption in IO is that an agent generates its decision by solving an optimization problem.
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Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards
Neural Information Processing Systems
Project lead, main contributor, correspondence to alexandre.rame@isir.upmc.fr. Equal experimental contribution, order determined at random. Further information and resources related to this project can be found on this website.
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