Michael I. Jordan

The paper introduces CalArena, a large-scale, standardized benchmark covering nearly 2000 experiments to comprehensively evaluate post-hoc calibration methods, finding that smooth calibration functions and dedicated multiclass approaches are superior.

The paper introduces a diffusion-based uncertainty model for robust optimization on graphs, showing that the resulting computational complexity depends critically on the interaction between the uncertainty propagation depth and the structure of the underlying combinatorial problem.