SCOREBED isolates EIG double intractability in a policy-independent score-matching stage, then trains design policies with a singly intractable gradient estimator, enabling cheap multi-policy selection.
Implicit Diffusion: Efficient Optimization through Stochastic Sampling
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling processes by optimizing over their parameters. We introduce a general framework for first-order optimization of these processes, that performs jointly, in a single loop, optimization and sampling steps. This approach is inspired by recent advances in bilevel optimization and automatic implicit differentiation, leveraging the point of view of sampling as optimization over the space of probability distributions. We provide theoretical guarantees on the performance of our method, as well as experimental results demonstrating its effectiveness. We apply it to training energy-based models and finetuning denoising diffusions.
years
2026 2representative citing papers
Diff2SP is a diffusion-based generative model that embeds stochastic optimization objectives into scenario generation and supplies regret bounds plus sample-complexity guarantees relative to GANs.
citing papers explorer
-
Bayesian Experimental Design via Score Matching
SCOREBED isolates EIG double intractability in a policy-independent score-matching stage, then trains design policies with a singly intractable gradient estimator, enabling cheap multi-policy selection.
-
Diff2SP: Diffusion Models for Correlated Scenario Generation in Stochastic Programming
Diff2SP is a diffusion-based generative model that embeds stochastic optimization objectives into scenario generation and supplies regret bounds plus sample-complexity guarantees relative to GANs.