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Variational Marginal Particle Filters


Sep 30, 2021
Jinlin Lai, Daniel Sheldon, Justin Domke


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Relaxed Marginal Consistency for Differentially Private Query Answering


Sep 13, 2021
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau


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Sibling Regression for Generalized Linear Models


Jul 07, 2021
Shiv Shankar, Daniel Sheldon


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The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data


Jun 13, 2021
Cheng Gu, Erik Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau


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Faster Kernel Interpolation for Gaussian Processes


Jan 28, 2021
Mohit Yadav, Daniel Sheldon, Cameron Musco

* To appear, Artificial Intelligence and Statistics (AISTATS) 2021 

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Three-quarter Sibling Regression for Denoising Observational Data


Dec 31, 2020
Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

* IJCAI 2019 

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Normalizing Flows Across Dimensions


Jun 23, 2020
Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, Daniel Sheldon


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Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization


Jun 18, 2020
Abhinav Agrawal, Daniel Sheldon, Justin Domke


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General-Purpose Differentially-Private Confidence Intervals


Jun 14, 2020
Cecilia Ferrando, Shufan Wang, Daniel Sheldon


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Detecting and Tracking Communal Bird Roosts in Weather Radar Data


Apr 24, 2020
Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler

* 9 pages, 6 figures, AAAI 2020 (AI for Social Impact Track) 

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Differentially Private Bayesian Linear Regression


Oct 29, 2019
Garrett Bernstein, Daniel Sheldon

* NeurIPS 2019 

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Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation


Jun 24, 2019
Justin Domke, Daniel Sheldon


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A Bayesian Perspective on the Deep Image Prior


Apr 16, 2019
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon

* CVPR 2019 

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Graphical-model based estimation and inference for differential privacy


Jan 26, 2019
Ryan McKenna, Daniel Sheldon, Gerome Miklau


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Importance Weighting and Variational Inference


Oct 27, 2018
Justin Domke, Daniel Sheldon

* Neural Information Processing Systems (NIPS) 2018 

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Differentially Private Bayesian Inference for Exponential Families


Oct 26, 2018
Garrett Bernstein, Daniel Sheldon

* NIPS 2018. Code available at https://github.com/gbernstein6/private_bayesian_expfam 

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Learning in Integer Latent Variable Models with Nested Automatic Differentiation


Jun 08, 2018
Daniel Sheldon, Kevin Winner, Debora Sujono


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Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models


Jun 14, 2017
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

* Accepted to ICML 2017 

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Robust Optimization for Tree-Structured Stochastic Network Design


Dec 01, 2016
Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein

* AAAI 2017 

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Bethe Projections for Non-Local Inference


Nov 28, 2016
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum

* minor bug fix to appendix. appeared in UAI 2015 

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Consistently Estimating Markov Chains with Noisy Aggregate Data


Apr 14, 2016
Garrett Bernstein, Daniel Sheldon

* AISTATS 2016 

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Gaussian Approximation of Collective Graphical Models


May 20, 2014
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich

* Accepted by ICML 2014. 10 page version with appendix 

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