Models, code, and papers for "Giles Strong":

Machine learning is an important research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.

We develop a framework that allows the use of the multi-level Monte Carlo (MLMC) methodology (Giles 2015) to calculate expectations with respect to the invariant measures of ergodic SDEs. In that context, we study the (over-damped) Langevin equations with strongly convex potential. We show that, when appropriate contracting couplings for the numerical integrators are available, one can obtain a time-uniform estimates of the MLMC variance in stark contrast to the majority of the results in the MLMC literature. As a consequence, one can approximate expectations with respect to the invariant measure in an unbiased way without the need of a Metropolis- Hastings step. In addition, a root mean square error of $\mathcal{O}(\epsilon)$ is achieved with $\mathcal{O}(\epsilon^{-2})$ complexity on par with Markov Chain Monte Carlo (MCMC) methods, which however can be computationally intensive when applied to large data sets. Finally, we present a multilevel version of the recently introduced Stochastic Gradient Langevin (SGLD) method (Welling and Teh, 2011) built for large datasets applications. We show that this is the first stochastic gradient MCMC method with complexity $\mathcal{O}(\epsilon^{-2}|\log {\epsilon}|^{3})$, which is asymptotically an order $\epsilon$ lower than the $ \mathcal{O}(\epsilon^{-3})$ complexity of all stochastic gradient MCMC methods that are currently available. Numerical experiments confirm our theoretical findings.

This paper advocates against permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures proposed for random forests, partial dependence plots, and individual conditional expectation plots remain popular because of their ability to provide model-agnostic measures that depend only on the pre-trained model output. However, numerous studies have found that these tools can produce diagnostics that are highly misleading, particularly when there is strong dependence among features. Rather than simply add to this growing literature by further demonstrating such issues, here we seek to provide an explanation for the observed behavior. In particular, we argue that breaking dependencies between features in hold-out data places undue emphasis on sparse regions of the feature space by forcing the original model to extrapolate to regions where there is little to no data. We explore these effects through various settings where a ground-truth is understood and find support for previous claims in the literature that PaP metrics tend to over-emphasize correlated features both in variable importance and partial dependence plots, even though applying permutation methods to the ground-truth models do not. As an alternative, we recommend more direct approaches that have proven successful in other settings: explicitly removing features, conditional permutations, or model distillation methods.