15 citations · 38 across the 8 of their papers we have counts for
8 papers
Quasiprobabilistic Density Ratio Estimation with a Reverse Engineered Classification Loss Function
Matthew Drnevich, Stephen Jiggins, Kyle Cranmer
We consider a generalization of the classifier-based density-ratio estimation task to a quasiprobabilistic setting where probability densities can be negative. The problem with mos…
The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects
Sebastian Macaluso, Kyle Cranmer
Simulations of high-energy particle collisions, such as those used at the Large Hadron Collider, are based on quantum field theory; however, many approximations are made in practic…
Reframing Jet Physics with New Computational Methods
Kyle Cranmer, Matthew Drnevich, Sebastian Macaluso +1
We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element - parton shower matching for large jet multiplicity,…
Exact and Approximate Hierarchical Clustering Using A*
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +6
Hierarchical clustering is a critical task in numerous domains. Many approaches are based on heuristics and the properties of the resulting clusterings are studied post hoc. Howeve…
Hierarchical clustering in particle physics through reinforcement learning
Johann Brehmer, Sebastian Macaluso, Duccio Pappadopulo +1
Particle physics experiments often require the reconstruction of decay patterns through a hierarchical clustering of the observed final-state particles. We show that this task can…
Data Structures & Algorithms for Exact Inference in Hierarchical Clustering
Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath +5
Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and casc…