19 citations · 69 across the 25 of their papers we have counts for
6 papers · 1 filter
Controlling Dependence in Implicit Generative Models via Spread Mutual Information
Jiahao Yu, Song Liu, José Miguel Hernández-Lobato +1
Mutual information (MI) provides an objective for suppressing or encouraging statistical dependence in implicit generative models. However, direct MI evaluation is challenging in i…
ENCORE: Exact Non-equilibrium COntrol with Replica Exchange for Diffusion Generation
Jiahao Yu, Saifuddin Syed, José Miguel Hernández-Lobato +1
Inference-time control steers a pretrained generative model towards a target distribution without retraining. We study tilted targets , where is the samp…
Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
Richard Bergna, Stefan Depeweg, Sergio Calvo Ordonez +3
Uncertainty quantification in neural networks through methods such as Dropout, Bayesian neural networks and Laplace approximations is either prone to underfitting or computationall…
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
Szilvia Ujváry, Gergely Flamich, Vincent Fortuin +1
An important yet underexplored question in the PAC-Bayes literature is how much tightness we lose by restricting the posterior family to factorized Gaussian distributions when opti…
Sampling-based inference for large linear models, with application to linearised Laplace
Javier Antorán, Shreyas Padhy, Riccardo Barbano +3
Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the…
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antorán, David Janz, James Urquhart Allingham +4
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community. The method provides reliable error bars and a…