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Variational Inference: A Review for Statisticians

4 January 2016

Papers citing "Variational Inference: A Review for Statisticians"

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Title
Knot Selection in Sparse Gaussian Processes with a Variational Objective Function Nathaniel Garton Jarad Niemi A. Carriquiry 40 2 0 05 Mar 2020
mmFall: Fall Detection using 4D MmWave Radar and a Hybrid Variational RNN AutoEncoder Feng Jin Arindam Sengupta Siyang Cao DRL 34 2 0 05 Mar 2020
Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network K. Yeo D. E. C. Grullon Fan-Keng Sun Duane S. Boning Jayant Kalagnanam BDL 57 3 0 02 Mar 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 109 95 0 02 Mar 2020
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization Jary Pomponi Simone Scardapane A. Uncini UQCV BDL 83 20 0 02 Mar 2020
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference Achille Thin Nikita Kotelevskii Jean-Stanislas Denain Léo Grinsztajn Alain Durmus Maxim Panov Eric Moulines BDL 55 17 0 27 Feb 2020
Gradient Boosted Normalizing Flows Robert Giaquinto A. Banerjee BDL DRL 31 1 0 27 Feb 2020
Composing Normalizing Flows for Inverse Problems Jay Whang Erik M. Lindgren A. Dimakis TPM 116 51 0 26 Feb 2020
Hierarchical clustering with discrete latent variable models and the integrated classification likelihood E. Côme Nicolas Jouvin Pierre Latouche C. Bouveyron 47 19 0 26 Feb 2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models Théo Galy-Fajou F. Wenzel Manfred Opper 60 4 0 26 Feb 2020
Lipschitz standardization for multivariate learning Adrián Javaloy Isabel Valera 31 0 0 26 Feb 2020
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering Yuan Jin He Zhao Ming Liu Ye Zhu Lan Du Longxiang Gao He Zhang Wray Buntine 187 0 0 21 Feb 2020
Stein Self-Repulsive Dynamics: Benefits From Past Samples Mao Ye Zhaolin Ren Qiang Liu 61 8 0 21 Feb 2020
Weakly-supervised Multi-output Regression via Correlated Gaussian Processes Seokhyun Chung Raed Al Kontar Zhenke Wu 39 5 0 19 Feb 2020
Gaussian Mixture Reduction with Composite Transportation Divergence Qiong Zhang Archer Gong Zhang Jiahua Chen 56 4 0 19 Feb 2020
Variational Bayesian Quantization Yibo Yang Robert Bamler Stephan Mandt BDL MQ 73 5 0 18 Feb 2020
Posterior Ratio Estimation of Latent Variables Song Liu Yulong Zhang Mingxuan Yi Mladen Kolar 71 2 0 15 Feb 2020
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities David Rügamer Chris Kolb Nadja Klein 79 23 0 13 Feb 2020
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees Jonas Rothfuss Vincent Fortuin Martin Josifoski Andreas Krause UQCV 93 127 0 13 Feb 2020
Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou Yitong Li Yuan Wu David Carlson UQCV 177 17 0 12 Feb 2020
Learnable Bernoulli Dropout for Bayesian Deep Learning Shahin Boluki Randy Ardywibowo Siamak Zamani Dadaneh Mingyuan Zhou Xiaoning Qian BDL 63 34 0 12 Feb 2020
Reward-rational (implicit) choice: A unifying formalism for reward learning Hong Jun Jeon S. Milli Anca Dragan 102 177 0 12 Feb 2020
Discrete Action On-Policy Learning with Action-Value Critic Yuguang Yue Yunhao Tang Mingzhang Yin Mingyuan Yin OffRL 78 5 0 10 Feb 2020
Projected Stein Variational Gradient Descent Peng Chen Omar Ghattas BDL 136 71 0 09 Feb 2020
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models Mohamed Tarek Kai Xu Martin Trapp Hong Ge Zoubin Ghahramani 46 7 0 07 Feb 2020
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks J. Swiatkowski Kevin Roth Bastiaan S. Veeling Linh-Tam Tran Joshua V. Dillon Jasper Snoek Stephan Mandt Tim Salimans Rodolphe Jenatton Sebastian Nowozin BDL 89 47 0 07 Feb 2020
Gaussian Variational State Estimation for Nonlinear State-Space Models Jarrad Courts A. Wills Thomas B. Schon 51 14 0 07 Feb 2020
Interpolating Predictors in High-Dimensional Factor Regression F. Bunea Seth Strimas-Mackey M. Wegkamp 73 12 0 06 Feb 2020
Forecasting Industrial Aging Processes with Machine Learning Methods Mihail Bogojeski Simeon Sauer F. Horn K. Müller AI4CE 40 21 0 05 Feb 2020
Variational Item Response Theory: Fast, Accurate, and Expressive Mike Wu R. Davis B. Domingue Chris Piech Noah D. Goodman OffRL 107 55 0 01 Feb 2020
Bayesian Reasoning with Trained Neural Networks Jakob Knollmüller T. Ensslin BDL UQCV 28 2 0 29 Jan 2020
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion Fabian Jirasek Rodrigo Alves J. Damay Robert A. Vandermeulen Robert Bamler Michael Bortz Stephan Mandt Marius Kloft Hans Hasse 244 69 0 29 Jan 2020
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis C. Sevilla-Salcedo Vanessa Gómez-Verdejo Pablo Martínez Olmos 39 11 0 24 Jan 2020
A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis Christopher J. Urban Daniel J. Bauer BDL 81 33 0 22 Jan 2020
Learning the Ising Model with Generative Neural Networks Francesco DÁngelo Lucas Böttcher AI4CE 57 28 0 15 Jan 2020
Unifying and generalizing models of neural dynamics during decision-making D. Zoltowski Jonathan W. Pillow Scott W. Linderman 48 8 0 13 Jan 2020
CATVI: Conditional and Adaptively Truncated Variational Inference for Hierarchical Bayesian Nonparametric Models Jones Yirui Liu Xinghao Qiao Jessica Lam TPM 115 3 0 13 Jan 2020
Learning credit assignment Chan Li Haiping Huang 69 7 0 10 Jan 2020
Variational Bayesian Methods for Stochastically Constrained System Design Problems Prateek Jaiswal Harsha Honnappa Vinayak A. Rao 32 2 0 06 Jan 2020
Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction Aravind Sankar Xinyang Zhang A. Krishnan Jiawei Han GNN 74 83 0 01 Jan 2020
Multilevel Monte Carlo estimation of log marginal likelihood T. Goda Kei Ishikawa 27 3 0 23 Dec 2019
Learning under Model Misspecification: Applications to Variational and Ensemble methods A. Masegosa 31 1 0 18 Dec 2019
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis Hannes Thaller L. Linsbauer Rudolf Ramler Alexander Egyed 125 4 0 17 Dec 2019
Testing Homogeneity for Normal Mixture Models: Variational Bayes Approach Natsuki Kariya Sumio Watanabe 26 1 0 15 Dec 2019
Parameter-Conditioned Sequential Generative Modeling of Fluid Flows Jeremy Morton F. Witherden Mykel J. Kochenderfer GAN MedIm AI4CE 61 10 0 14 Dec 2019
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection Erik A. Daxberger José Miguel Hernández-Lobato UQCV 103 63 0 11 Dec 2019
Bayesian Structure Adaptation for Continual Learning Abhishek Kumar Sunabha Chatterjee Piyush Rai CLL BDL 64 30 0 08 Dec 2019
Normalizing Flows for Probabilistic Modeling and Inference George Papamakarios Eric T. Nalisnick Danilo Jimenez Rezende S. Mohamed Balaji Lakshminarayanan TPM AI4CE 219 1,724 0 05 Dec 2019
Solving Bayesian Inverse Problems via Variational Autoencoders Hwan Goh Sheroze Sheriffdeen J. Wittmer T. Bui-Thanh BDL 147 39 0 05 Dec 2019
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning Hangyu Mao Wulong Liu Jianye Hao Jun Luo Dong Li Zhengchao Zhang Jun Wang Zhen Xiao OffRL 83 77 0 03 Dec 2019