Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions

22 March 2015

Papers citing "Large-scale Log-determinant Computation through Stochastic Chebyshev Expansions"

36 / 36 papers shown

Title
Analyzing Generative Models by Manifold Entropic Metrics Daniel Galperin Ullrich Köthe DRL 148 0 0 25 Oct 2024
Stochastic diagonal estimation with adaptive parameter selection Zongyuan Han Wenhao Li Shengxin Zhu 52 0 0 15 Oct 2024
Batch Active Learning in Gaussian Process Regression using Derivatives Hon Sum Alec Yu Christoph Zimmer D. Nguyen-Tuong GP 67 1 0 03 Aug 2024
On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models Christian Horvat J. Pfister DiffM 92 13 0 06 Feb 2024
The Exact Determinant of a Specific Class of Sparse Positive Definite Matrices Mehdi Molkaraie 15 0 0 11 Nov 2023
A Review of Change of Variable Formulas for Generative Modeling Ullrich Kothe 72 8 0 04 Aug 2023
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability J. Parkinson Wen Wang BDL 59 8 0 07 Feb 2023
Geostatistics for large datasets on Riemannian manifolds: a matrix-free approach M. Pereira N. Desassis D. Allard 34 10 0 26 Aug 2022
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder Jaemin Yoo Hyunsik Jeon Jinhong Jung U. Kang BDL 50 21 0 09 Jun 2022
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity Jian-Feng Cai José Vinícius de Miranda Cardoso Daniel P. Palomar Jiaxi Ying 98 11 0 03 Dec 2021
Rectangular Flows for Manifold Learning Anthony L. Caterini Gabriel Loaiza-Ganem Geoff Pleiss John P. Cunningham DRL 105 47 0 02 Jun 2021
Large-Scale Wasserstein Gradient Flows Petr Mokrov Alexander Korotin Lingxiao Li Aude Genevay Justin Solomon Evgeny Burnaev 90 76 0 01 Jun 2021
Geometric variational inference Philipp Frank R. Leike T. Ensslin 76 24 0 21 May 2021
Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression Christian Fiedler C. Scherer Sebastian Trimpe GP 77 71 0 06 May 2021
Quantum algorithms for spectral sums Alessandro Luongo Changpeng Shao 53 5 0 12 Nov 2020
Hutch++: Optimal Stochastic Trace Estimation R. A. Meyer Cameron Musco Christopher Musco David P. Woodruff 98 106 0 19 Oct 2020
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs Ignavier Ng AmirEmad Ghassami Kun Zhang CML 89 189 0 17 Jun 2020
Two equalities expressing the determinant of a matrix in terms of expectations over matrix-vector products Jascha Narain Sohl-Dickstein 11 5 0 13 May 2020
MEMe: An Accurate Maximum Entropy Method for Efficient Approximations in Large-Scale Machine Learning Diego Granziol Binxin Ru S. Zohren Xiaowen Dong Michael A. Osborne Stephen J. Roberts 55 20 0 03 Jun 2019
Exponential Family Estimation via Adversarial Dynamics Embedding Bo Dai Ziqiang Liu H. Dai Niao He Arthur Gretton Le Song Dale Schuurmans 82 53 0 27 Apr 2019
Scaling Gaussian Process Regression with Derivatives David Eriksson Kun Dong E. Lee D. Bindel A. Wilson GP 63 76 0 29 Oct 2018
Entropic Spectral Learning for Large-Scale Graphs Diego Granziol Binxin Ru S. Zohren Xiaowen Dong Michael A. Osborne Stephen J. Roberts 14 3 0 18 Apr 2018
VBALD - Variational Bayesian Approximation of Log Determinants Diego Granziol E. Wagstaff Binxin Ru Michael A. Osborne Stephen J. Roberts 42 2 0 21 Feb 2018
Stochastic Chebyshev Gradient Descent for Spectral Optimization Insu Han H. Avron Jinwoo Shin 67 11 0 18 Feb 2018
Estimating the Spectral Density of Large Implicit Matrices Ryan P. Adams Jeffrey Pennington Matthew J. Johnson Jamie Smith Yaniv Ovadia Brian Patton J. Saunderson 90 34 0 09 Feb 2018
Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong David Eriksson H. Nickisch D. Bindel A. Wilson 69 95 0 09 Nov 2017
A determinant-free method to simulate the parameters of large Gaussian fields L. Ellam Heiko Strathmann Mark Girolami Iain Murray 96 3 0 11 Sep 2017
A Nonparametric Model for Multimodal Collaborative Activities Summarization Guy Rosman John W. Fisher III Daniela Rus EgoV 22 0 0 04 Sep 2017
Large Linear Multi-output Gaussian Process Learning Vladimir Feinberg Li-Fang Cheng Kai Li Barbara E. Engelhardt GP 42 6 0 30 May 2017
Entropic Trace Estimates for Log Determinants Jack K. Fitzsimons Diego Granziol Kurt Cutajar Michael A. Osborne Maurizio Filippone Stephen J. Roberts 51 26 0 24 Apr 2017
Bayesian Inference of Log Determinants Jack K. Fitzsimons Kurt Cutajar Michael A. Osborne Stephen J. Roberts Maurizio Filippone 87 18 0 05 Apr 2017
Spectrum Estimation from a Few Entries A. Khetan Sewoong Oh 47 8 0 18 Mar 2017
Faster Greedy MAP Inference for Determinantal Point Processes Insu Han P. Kambadur KyoungSoo Park Jinwoo Shin 71 25 0 09 Mar 2017
Nonparanormal Information Estimation Shashank Singh Barnabás Póczós 134 20 0 24 Feb 2017
Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces William Herlands A. Wilson H. Nickisch Seth Flaxman Daniel B. Neill Wilbert Van Panhuis Eric Xing 36 32 0 13 Nov 2015
Teaching and compressing for low VC-dimension Shay Moran Amir Shpilka Avi Wigderson Amir Yehudayoff 86 12 0 22 Feb 2015