A Heavy-Tailed Algebra for Probabilistic Programming

15 June 2023

Papers citing "A Heavy-Tailed Algebra for Probabilistic Programming"

19 / 19 papers shown

Title
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows Feynman T. Liang Liam Hodgkinson Michael W. Mahoney 48 11 0 16 May 2022
The Heavy-Tail Phenomenon in SGD Mert Gurbuzbalaban Umut Simsekli Lingjiong Zhu 45 126 0 08 Jun 2020
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 202 1,691 0 05 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 475 42,407 0 03 Dec 2019
Static Analysis for Probabilistic Programs Ryan Bernstein TPM 47 20 0 10 Sep 2019
Towards Verified Stochastic Variational Inference for Probabilistic Programs Wonyeol Lee Hangyeol Yu Xavier Rival Hongseok Yang 41 26 0 20 Jul 2019
Neural Spline Flows Conor Durkan Artur Bekasov Iain Murray George Papamakarios DRL 174 774 0 10 Jun 2019
A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks Umut Simsekli Levent Sagun Mert Gurbuzbalaban 85 247 0 18 Jan 2019
Simple, Distributed, and Accelerated Probabilistic Programming Like Hui Matthew Hoffman Siyuan Ma Christopher Suter Srinivas Vasudevan Alexey Radul M. Belkin Rif A. Saurous BDL 58 56 0 05 Nov 2018
Variational Inference with Tail-adaptive f-Divergence Dilin Wang Hao Liu Qiang Liu 44 55 0 29 Oct 2018
Pyro: Deep Universal Probabilistic Programming Eli Bingham Jonathan P. Chen M. Jankowiak F. Obermeyer Neeraj Pradhan Theofanis Karaletsos Rohit Singh Paul A. Szerlip Paul Horsfall Noah D. Goodman BDL GP 155 1,053 0 18 Oct 2018
Yes, but Did It Work?: Evaluating Variational Inference Yuling Yao Aki Vehtari Daniel P. Simpson Andrew Gelman 52 136 0 07 Feb 2018
Learning Disentangled Representations with Semi-Supervised Deep Generative Models Siddharth Narayanaswamy Brooks Paige Jan-Willem van de Meent Alban Desmaison Noah D. Goodman Pushmeet Kohli Frank Wood Philip Torr DRL CoGe 117 362 0 01 Jun 2017
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models S. M. Ali Eslami N. Heess T. Weber Yuval Tassa David Szepesvari Koray Kavukcuoglu Geoffrey E. Hinton 3DV BDL OCL 122 550 0 28 Mar 2016
Automatic Differentiation Variational Inference A. Kucukelbir Dustin Tran Rajesh Ranganath Andrew Gelman David M. Blei 106 717 0 02 Mar 2016
Importance Weighted Autoencoders Yuri Burda Roger C. Grosse Ruslan Salakhutdinov BDL 268 1,245 0 01 Sep 2015
Pareto Smoothed Importance Sampling Aki Vehtari Daniel Simpson Andrew Gelman Yuling Yao Jonah Gabry 66 242 0 09 Jul 2015
Programming with models: writing statistical algorithms for general model structures with NIMBLE P. de Valpine Daniel Turek C. Paciorek Clifford Anderson-Bergman D. Lang Rastislav Bodík 59 854 0 19 May 2015
Church: a language for generative models Noah D. Goodman Vikash K. Mansinghka Daniel M. Roy Keith Bonawitz J. Tenenbaum 100 817 0 13 Jun 2012