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Found 4 papers, 2 papers with code
Causal Optimal Transport of Abstractions
Causal abstraction (CA) theory establishes formal criteria for relating multiple structural causal models (SCMs) at different levels of granularity by defining maps between them.
Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization
We then show that the $\alpha$-divergence can be approximated by a generalized notion of effective sample size and leverage this new perspective to adapt the tail parameter with Bayesian optimization.
Causal Entropy Optimization
We study the problem of globally optimizing the causal effect on a target variable of an unknown causal graph in which interventions can be performed.
Optimized Auxiliary Particle Filters: adapting mixture proposals via convex optimization
In this work, we propose optimized auxiliary particle filters, a framework where the traditional APF auxiliary variables are interpreted as weights in an importance sampling mixture proposal.
Computation
