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Found 8 papers, 4 papers with code
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them.
Flexible model composition in machine learning and its implementation in MLJ
A graph-based protocol called `learning networks' which combine assorted machine learning models into meta-models is described.
MLJ: A Julia package for composable machine learning
MLJ (Machine Learing in Julia) is an open source software package providing a common interface for interacting with machine learning models written in Julia and other languages.
Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration.
Methodology Computation
Measuring Sample Quality with Diffusions
Stein's method for measuring convergence to a continuous target distribution relies on an operator characterizing the target and Stein factor bounds on the solutions of an associated differential equation.
Relativistic Monte Carlo
Based on this, we develop relativistic stochastic gradient descent by taking the zero-temperature limit of relativistic stochastic gradient Hamiltonian Monte Carlo.
The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
We explore and propose several original extensions of an alternative approach introduced recently in Peters and de With (2012) where the target distribution of interest is explored using a continuous-time Markov process.
Methodology Statistics Theory Statistics Theory
(Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
For this toy model we study the gain of the SGLD over the standard Euler method in the limit of large data sets.
