Search Results for author:
Found 8 papers, 8 papers with code
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution.
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations.
Towards constraining warm dark matter with stellar streams through neural simulation-based inference
A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent contraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle.
Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
The subtle and unique imprint of dark matter substructure on extended arcs in strong lensing systems contains a wealth of information about the properties and distribution of dark matter on small scales and, consequently, about the underlying particle physics.
Likelihood-free MCMC with Amortized Approximate Ratio Estimators
This work introduces a novel approach to address the intractability of the likelihood and the marginal model.
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers.
Accumulated Gradient Normalization
This work addresses the instability in asynchronous data parallel optimization.
Adversarial Variational Optimization of Non-Differentiable Simulators
We adapt the training procedure of generative adversarial networks by replacing the differentiable generative network with a domain-specific simulator.
