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.
Optimal Scaling of Discrete Approximations to Langevin Diffusions
6 Pith papers cite this work, alongside 648 external citations. Polarity classification is still indexing.
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A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.
Compares MH, MALA, HMC, NUTS, and AIES on differentiable likelihood emulators for ΛCDM and sterile-neutrino models, finding MALA and MH competitive in wall time despite NUTS needing fewer samples.
citing papers explorer
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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.
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Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
A homotopy-plus-MCMC data-generation pipeline trains a mass-conditioned diffusion model that yields 40% more feasible initial costates and a better Pareto front for multiobjective indirect low-thrust transfers than adjoint-control-transformation baselines.
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Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
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Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
E-value sequential tests enable early stopping of MCMC sampling in Bayesian deep ensembles, often needing only a fraction of the full budget while improving over standard deep ensembles.
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A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data
Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.
-
Posterior sampling in the Age of Emulators
Compares MH, MALA, HMC, NUTS, and AIES on differentiable likelihood emulators for ΛCDM and sterile-neutrino models, finding MALA and MH competitive in wall time despite NUTS needing fewer samples.