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Found 6 papers, 2 papers with code
Learning Rate Free Sampling in Constrained Domains
We introduce a suite of new particle-based algorithms for sampling in constrained domains which are entirely learning rate free.
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Our methods are based on the perspective of marginal maximum likelihood estimation as an optimization problem: namely, as the minimization of a free energy functional.
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent (SVGD) have grown in popularity as scalable methods for Bayesian inference.
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
We introduce Sequential Neural Posterior Score Estimation (SNPSE) and Sequential Neural Likelihood Score Estimation (SNLSE), two new score-based methods for Bayesian inference in simulator-based models.
Two-Timescale Stochastic Approximation for Bilevel Optimisation Problems in Continuous-Time Models
We analyse the asymptotic properties of a continuous-time, two-timescale stochastic approximation algorithm designed for stochastic bilevel optimisation problems in continuous-time models.
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
In particular, we consider training a quantum circuit Born machine using $f$-divergences.
