Goto

Collaborating Authors

 Europe


Sparse or

Neural Information Processing Systems

Table evaluated hyperparameters Dataset Nd GPR |M| - - q() - - free-form Boston 506 13 3.049 Concrete 1030 8 4.864 Ener 768 8 0.441 WineRed1599 11 0.640 Yacht308 6 0.353




FindingRegionsofHeterogeneityinDecision-Making viaExpectedConditionalCovariance

Neural Information Processing Systems

Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients.


WhenDoFlatMinimaOptimizers Work?

Neural Information Processing Systems

Theoretical and empirical studies [21,77,9,55,49,5,12]postulate that such flatter regions generalize better than sharper minima, e.g., due to the flat minimizer's robustness against loss function shifts between trainandtestdata,asillustrated inFig.1.