Our preliminary results suggest that the regularized exponential mechanism can effectively emulate previous empirical and population risk bounds, negating the need for smoothness assumptions for algorithms with polynomial running time.

U, which achieves an unbiased stochastic approximation of the meta gradient for bi-level optimization.

However, these methods typically reach superlinear convergence only when the stochastic Hessian noise diminishes, increasing per-iteration costs over time.

We establish a mathematical framework for guided diffusion to systematically study its optimization theory and algorithmic design.

For this reason, the ML community started focusing on non-monotone problems with extra structural properties.

This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constraints serve as conditioning variables.