p7stat.CO

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stat.CO

150 papers

Variational Dropout and the Local Reparameterization Trick

Diederik P. Kingma, Tim Salimans, Max Welling

1506.02557

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Dustin Tran, Rajesh Ranganath, David M. Blei

1702.08896

Array Programming with NumPy

Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, Travis E. Oliphant

2006.10256

A randomized algorithm for principal component analysis

Vladimir Rokhlin, Arthur Szlam, Mark Tygert

0809.2274

Deep Poisson gamma dynamical systems

Dandan Guo, Bo Chen, Hao Zhang, Mingyuan Zhou

1810.11209

Message Passing Algorithms for Compressed Sensing

David L. Donoho, Arian Maleki, Andrea Montanari

0907.3574

WHAI: Weibull Hybrid Autoencoding Inference for Deep Topic Modeling

Hao Zhang, Bo Chen, Dandan Guo, Mingyuan Zhou

1803.01328

Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC

Yulai Cong, Bo Chen, Hongwei Liu, Mingyuan Zhou

1706.01724

Fast Randomized Kernel Methods With Statistical Guarantees

Ahmed El Alaoui, Michael W. Mahoney

1411.0306

Variational Inference with Normalizing Flows

Danilo Jimenez Rezende, Shakir Mohamed

1505.05770

A Linearly-Convergent Stochastic L-BFGS Algorithm

Philipp Moritz, Robert Nishihara, Michael I. Jordan

1508.02087

Accelerated Proximal Stochastic Dual Coordinate Ascent for Regularized Loss Minimization

Shai Shalev-Shwartz, Tong Zhang

1309.2375

Minimizing Finite Sums with the Stochastic Average Gradient

Mark Schmidt, Nicolas Le Roux, Francis Bach

1309.2388

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields

Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar

1504.04406

Observed Universality of Phase Transitions in High-Dimensional Geometry, with Implications for Modern Data Analysis and Signal Processing

David L. Donoho, Jared Tanner

0906.2530

Layered Adaptive Importance Sampling

L. Martino, V. Elvira, D. Luengo, J. Corander

1505.04732

Particle Filters for Partially Observed Diffusions

Paul Fearnhead, Omiros Papaspiliopoulos, Gareth Roberts

0710.4245

Probabilistic Numerics and Uncertainty in Computations

Philipp Hennig, Michael A Osborne, Mark Girolami

1506.01326

A Framework for Evaluating Approximation Methods for Gaussian Process Regression

Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray

1205.6326

Fast Simulation of Hyperplane-Truncated Multivariate Normal Distributions

Yulai Cong, Bo Chen, Mingyuan Zhou

1607.04751

Particle Gibbs for Bayesian Additive Regression Trees

Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh

1502.04622

Dynamic Trees for Learning and Design

Matthew A. Taddy, Robert B. Gramacy, Nicholas G. Polson

0912.1586

Particle learning of Gaussian process models for sequential design and optimization

Robert B. Gramacy, Nicholas G. Polson

0909.5262

The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting

Z. I. Botev

1603.04166

Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

Sungjin Ahn, Anoop Korattikara, Max Welling

1206.6380

Sum-of-Squares Lower Bounds for Sparse PCA

Tengyu Ma, Avi Wigderson

1507.06370

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions

Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

1511.00146

Variational Inference: A Review for Statisticians

David M. Blei, Alp Kucukelbir, Jon D. McAuliffe

1601.00670

Black Box Variational Inference

Rajesh Ranganath, Sean Gerrish, David M. Blei

1401.0118

Edward: A library for probabilistic modeling, inference, and criticism

Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei

1610.09787

Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

George Papamakarios, Iain Murray

1605.06376

Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels

Haim Avron, Vikas Sindhwani, Jiyan Yang, Michael Mahoney

1412.8293

Provable Bayesian Inference via Particle Mirror Descent

Bo Dai, Niao He, Hanjun Dai, Le Song

1506.03101

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Matthew D. Hoffman, Andrew Gelman

1111.4246

An algorithm for the principal component analysis of large data sets

Nathan Halko, Per-Gunnar Martinsson, Yoel Shkolnisky, Mark Tygert

1007.5510

A Scalable Bootstrap for Massive Data

Ariel Kleiner, Ameet Talwalkar, Purnamrita Sarkar, Michael I. Jordan

1112.5016

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava

1806.04854

A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints

Nicolas Boumal

1506.00575

Approximate Gradient Coding via Sparse Random Graphs

Zachary Charles, Dimitris Papailiopoulos, Jordan Ellenberg

1711.06771

Stochastic Variational Inference

Matt Hoffman, David M. Blei, Chong Wang, John Paisley

1206.7051

Hierarchical Variational Models

Rajesh Ranganath, Dustin Tran, David M. Blei

1511.02386

Interacting Particle Markov Chain Monte Carlo

Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank Wood

1602.05128

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition

Hamed Karimi, Julie Nutini, Mark Schmidt

1608.04636

Slice sampling covariance hyperparameters of latent Gaussian models

Iain Murray, Ryan Prescott Adams

1006.0868

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra

1401.4082

Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression

Tim Salimans, David A. Knowles

1206.6679

Variational Sequential Monte Carlo

Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei

1705.11140

Variational Bayesian Inference with Stochastic Search

John Paisley, David Blei, Michael Jordan

1206.6430

Firefly Monte Carlo: Exact MCMC with Subsets of Data

Dougal Maclaurin, Ryan P. Adams

1403.5693

Automatic Differentiation Variational Inference

Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei

1603.00788

Learning Neural PDE Solvers with Convergence Guarantees

Jun-Ting Hsieh, Shengjia Zhao, Stephan Eismann, Lucia Mirabella, Stefano Ermon

1906.01200

Accelerating ABC methods using Gaussian processes

Richard D Wilkinson

1401.1436

Likelihood-free inference via classification

Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander

1407.4981

Efficient Metropolis-Hastings Proposal Mechanisms for Bayesian Regression Tree Models

M. T. Pratola

1312.1895

Operator Variational Inference

Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei

1610.09033

Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm

Alain Durmus, Eric Moulines

1507.05021

Elliptical slice sampling

Iain Murray, Ryan Prescott Adams, David J. C. MacKay

1001.0175

Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Fernando V. Bonassi, Mike West

1503.07791

Information Dropout: Learning Optimal Representations Through Noisy Computation

Alessandro Achille, Stefano Soatto

1611.01353

Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection

Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael Friedlander, Hoyt Koepke

1506.00552

Parallel Bayesian Additive Regression Trees

Matthew T. Pratola, Hugh A. Chipman, James R. Gattiker, David M. Higdon, Robert McCulloch, William N. Rust

1309.1906

Gaussian variational approximation with sparse precision matrices

Linda S. L. Tan, David J. Nott

1605.05622

Theoretical guarantees for approximate sampling from smooth and log-concave densities

Arnak S. Dalalyan

1412.7392

Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems

Thomas Bengtsson, Peter Bickel, Bo Li

0805.3034

Nested Sequential Monte Carlo Methods

Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

1502.02536

Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent

Trevor Campbell, Tamara Broderick

1802.01737

MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems

Sou-Cheng T. Choi, Christopher C. Paige, Michael A. Saunders

1003.4042

Accelerating MCMC Algorithms

Christian P. Robert, Victor Elvira, Nick Tawn, Changye Wu

1804.02719

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

Zhuang Ma, Yichao Lu, Dean Foster

1506.08170

Maximum likelihood estimation of a multidimensional log-concave density

Madeleine Cule, Richard Samworth, Michael Stewart

0804.3989

Fast sampling with Gaussian scale-mixture priors in high-dimensional regression

Anirban Bhattacharya, Antik Chakraborty, Bani K. Mallick

1506.04778

On Nesting Monte Carlo Estimators

Tom Rainforth, Robert Cornish, Hongseok Yang, Andrew Warrington, Frank Wood

1709.06181

Excursion and contour uncertainty regions for latent Gaussian models

David Bolin, Finn Lindgren

1211.3946

Robust Estimation and Generative Adversarial Nets

Chao Gao, Jiyi Liu, Yuan Yao, Weizhi Zhu

1810.02030

Stop Wasting My Gradients: Practical SVRG

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konečný, Scott Sallinen

1511.01942

The Variational Gaussian Process

Dustin Tran, Rajesh Ranganath, David M. Blei

1511.06499

Global analysis of Expectation Maximization for mixtures of two Gaussians

Ji Xu, Daniel Hsu, Arian Maleki

1608.07630

Random Feature Expansions for Deep Gaussian Processes

Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone

1610.04386

Bayesian treed Gaussian process models with an application to computer modeling

Robert B. Gramacy, Herbert K. H. Lee

0710.4536

Gradient Coding

Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis

1612.03301

Preconditioning Kernel Matrices

Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone

1602.06693

Deep Probabilistic Programming

Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

1701.03757

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Richard G. Everitt

1203.3725

Bayesian Optimization for Probabilistic Programs

Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank Wood

1707.04314

Expectation-Propagation for Likelihood-Free Inference

Simon Barthelmé, Nicolas Chopin

1107.5959

Information-geometric Markov Chain Monte Carlo methods using Diffusions

Samuel Livingstone, Mark Girolami

1403.7957

Proximal Markov chain Monte Carlo algorithms

Marcelo Pereyra

1306.0187

Particle Gibbs with Ancestor Sampling

Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

1401.0604

Bayesian inference for logistic models using Polya-Gamma latent variables

Nicholas G. Polson, James G. Scott, Jesse Windle

1205.0310

Analysis of nonsmooth stochastic approximation: the differential inclusion approach

Szymon Majewski, Błażej Miasojedow, Eric Moulines

1805.01916

Automated Scalable Bayesian Inference via Hilbert Coresets

Trevor Campbell, Tamara Broderick

1710.05053

Accelerating Asymptotically Exact MCMC for Computationally Intensive Models via Local Approximations

Patrick R. Conrad, Youssef M. Marzouk, Natesh S. Pillai, Aaron Smith

1402.1694

Global Non-convex Optimization with Discretized Diffusions

Murat A. Erdogdu, Lester Mackey, Ohad Shamir

1810.12361

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Tina Toni, David Welch, Natalja Strelkowa, Andreas Ipsen, Michael P. H. Stumpf

0901.1925

An introduction to sampling via measure transport

Youssef Marzouk, Tarek Moselhy, Matthew Parno, Alessio Spantini

1602.05023

Efficient Bayesian Inference for Generalized Bradley-Terry Models

Francois Caron, Arnaud Doucet

1011.1761

Asynchronous Anytime Sequential Monte Carlo

Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

1407.2864

Sparse Covers for Sums of Indicators

Constantinos Daskalakis, Christos Papadimitriou

1306.1265

Min-Max Kernels

Ping Li

1503.01737

KFAS: Exponential Family State Space Models in R

Jouni Helske

1612.01907

High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm

Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, Michael I. Jordan

1908.10859

A* Sampling

Chris J. Maddison, Daniel Tarlow, Tom Minka

1411.0030

Path storage in the particle filter

Pierre E. Jacob, Lawrence Murray, Sylvain Rubenthaler

1307.3180

Data-Driven Model Reduction for the Bayesian Solution of Inverse Problems

Tiangang Cui, Youssef M. Marzouk, Karen E. Willcox

1403.4290

SMC^2: an efficient algorithm for sequential analysis of state-space models

Nicolas Chopin, Pierre E. Jacob, Omiros Papaspiliopoulos

1101.1528

On the efficiency of pseudo-marginal random walk Metropolis algorithms

Chris Sherlock, Alexandre H. Thiery, Gareth O. Roberts, Jeffrey S. Rosenthal

1309.7209

On the Geometric Ergodicity of Hamiltonian Monte Carlo

Samuel Livingstone, Michael Betancourt, Simon Byrne, Mark Girolami

1601.08057

Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets

Natesh S. Pillai, Aaron Smith

1405.0182

Pac-bayesian bounds for sparse regression estimation with exponential weights

Pierre Alquier, Karim Lounici

1009.2707

Randomized algorithms for low-rank matrix factorizations: sharp performance bounds

Rafi Witten, Emmanuel Candes

1308.5697

Parallel resampling in the particle filter

Lawrence M. Murray, Anthony Lee, Pierre E. Jacob

1301.4019

Local Gaussian process approximation for large computer experiments

Robert B. Gramacy, Daniel W. Apley

1303.0383

Delayed Sampling and Automatic Rao-Blackwellization of Probabilistic Programs

Lawrence M. Murray, Daniel Lundén, Jan Kudlicka, David Broman, Thomas B. Schön

1708.07787

Practical Lossless Compression with Latent Variables using Bits Back Coding

James Townsend, Tom Bird, David Barber

1901.04866

Simulation-based Regularized Logistic Regression

Robert B. Gramacy, Nicholas G. Polson

1005.3430

Bayesian computation via empirical likelihood

K. L. Mengersen, P. Pudlo, C. P. Robert

1205.5658

Detecting the overlapping and hierarchical community structure of complex networks

Andrea Lancichinetti, Santo Fortunato, Janos Kertesz

0802.1218

On some difficulties with a posterior probability approximation technique

Christian Robert, Jean-Michel Marin

0801.3513

Sandwiching the marginal likelihood using bidirectional Monte Carlo

Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

1511.02543

Evolutionary Stochastic Search for Bayesian model exploration

Leonardo Bottolo, Sylvia Richardson

1002.2706

Identifying the Optimal Integration Time in Hamiltonian Monte Carlo

Michael Betancourt

1601.00225

Perfect simulation using atomic regeneration with application to Sequential Monte Carlo

Anthony Lee, Arnaud Doucet, Krzysztof Łatuszyński

1407.5770

Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

Michael U. Gutmann, Jukka Corander

1501.03291

Variance components and generalized Sobol' indices

Art B. Owen

1205.1774

Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models

Mohammad Emtiyaz Khan, Didrik Nielsen

1807.04489

The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data

Joris Bierkens, Paul Fearnhead, Gareth Roberts

1607.03188

Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains

Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer

1701.04244

A Review of Multiple Try MCMC algorithms for Signal Processing

Luca Martino

1801.09065

Unbiased Markov chain Monte Carlo with couplings

Pierre E. Jacob, John O'Leary, Yves F. Atchadé

1708.03625

Statistical properties of sketching algorithms

Daniel Ahfock, William J. Astle, Sylvia Richardson

1706.03665

Convergence properties of the expected improvement algorithm

Emmanuel Vazquez, Julien Bect

0712.3744

Monotonic convergence of a general algorithm for computing optimal designs

Yaming Yu

0905.2646

An implementation of a randomized algorithm for principal component analysis

Arthur Szlam, Yuval Kluger, Mark Tygert

1412.3510

MCMC using Hamiltonian dynamics

Radford M. Neal

1206.1901

Variational inference for large-scale models of discrete choice

Michael Braun, Jon McAuliffe

0712.2526

Riemannian Manifold Hamiltonian Monte Carlo

Mark Girolami, Ben Calderhead, Siu A. Chin

0907.1100

Unbiased estimation of log normalizing constants with applications to Bayesian cross-validation

Maxime Rischard, Pierre E. Jacob, Natesh Pillai

1810.01382

Outlier Detection Using Nonconvex Penalized Regression

Yiyuan She, Art B. Owen

1006.2592

Conditional Gradient Algorithms for Norm-Regularized Smooth Convex Optimization

Zaid Harchaoui, Anatoli Juditsky, Arkadi Nemirovski

1302.2325

Exponential Ergodicity of the Bouncy Particle Sampler

George Deligiannidis, Alexandre Bouchard-Côté, Arnaud Doucet

1705.04579

On Markov chain Monte Carlo methods for tall data

Rémi Bardenet, Arnaud Doucet, Chris Holmes

1505.02827

Cooperative Parallel Particle Filters for online model selection and applications to Urban Mobility

Luca Martino, Jesse Read, Victor Elvira, Francisco Louzada

1609.07731

Effective Sample Size for Importance Sampling based on discrepancy measures

L. Martino, V. Elvira, F. Louzada

1602.03572

Metropolis Sampling

Luca Martino, Victor Elvira

1704.04629

Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap

Miles E. Lopes, Shusen Wang, Michael W. Mahoney

1803.08021

Limit theorems for the Zig-Zag process

Joris Bierkens, Andrew Duncan

1607.08845

Rapid Mixing of Hamiltonian Monte Carlo on Strongly Log-Concave Distributions

Oren Mangoubi, Aaron Smith

1708.07114

Simple, Scalable and Accurate Posterior Interval Estimation

Cheng Li, Sanvesh Srivastava, David B. Dunson

1605.04029

Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo

Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth O Roberts

1611.07873

Entropy, Optimization and Counting

Mohit Singh, Nisheeth K. Vishnoi

1304.8108