Cranmer, Kyle

9 publications

AISTATS 2021 Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering Sebastian Macaluso, Craig Greenberg, Nicholas Monath, Ji Ah Lee, Patrick Flaherty, Kyle Cranmer, Andrew McGregor, Andrew McCallum

UAI 2021 Exact and Approximate Hierarchical Clustering Using A* Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum

NeurIPS 2020 Discovering Symbolic Models from Deep Learning with Inductive Biases Miles Cranmer, Alvaro Sanchez Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho

NeurIPS 2020 Flows for Simultaneous Manifold Learning and Density Estimation Johann Brehmer, Kyle Cranmer

ICML 2020 Normalizing Flows on Tori and Spheres Danilo Jimenez Rezende, George Papamakarios, Sebastien Racaniere, Michael Albergo, Gurtej Kanwar, Phiala Shanahan, Kyle Cranmer

NeurIPS 2020 Set2Graph: Learning Graphs from Sets Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman

AISTATS 2019 Adversarial Variational Optimization of Non-Differentiable Simulators Gilles Louppe, Joeri Hermans, Kyle Cranmer

NeurIPS 2019 Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip Torr, Victor Lee, Kyle Cranmer, Mr. Prabhat, Frank Wood

NeurIPS 2017 Learning to Pivot with Adversarial Networks Gilles Louppe, Michael Kagan, Kyle Cranmer