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06/05/2023
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling
Many modern applications use computer vision to detect and count objects...
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05/23/2023
Kernel Interpolation with Sparse Grids
Structured kernel interpolation (SKI) accelerates Gaussian process (GP) ...
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04/13/2023
Sample Average Approximation for Black-Box VI
We present a novel approach for black-box VI that bypasses the difficult...
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02/27/2023
U-Statistics for Importance-Weighted Variational Inference
We propose the use of U-statistics to reduce variance for gradient estim...
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02/01/2023
Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent v...
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01/29/2022
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data
We propose AIM, a novel algorithm for differentially private synthetic d...
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09/30/2021
Variational Marginal Particle Filters
Variational inference for state space models (SSMs) is known to be hard ...
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09/13/2021
Relaxed Marginal Consistency for Differentially Private Query Answering
Many differentially private algorithms for answering database queries in...
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08/11/2021
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data
We propose a general approach for differentially private synthetic data ...
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07/03/2021
Sibling Regression for Generalized Linear Models
Field observations form the basis of many scientific studies, especially...
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06/13/2021
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
Event cameras, inspired by biological vision systems, provide a natural ...
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01/28/2021
Faster Kernel Interpolation for Gaussian Processes
A key challenge in scaling Gaussian Process (GP) regression to massive d...
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12/31/2020
Three-quarter Sibling Regression for Denoising Observational Data
Many ecological studies and conservation policies are based on field obs...
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10/23/2020
Permute-and-Flip: A new mechanism for differentially private selection
We consider the problem of differentially private selection. Given a fin...
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06/23/2020
Normalizing Flows Across Dimensions
Real-world data with underlying structure, such as pictures of faces, ar...
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06/18/2020
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
Recent research has seen several advances relevant to black-box VI, but ...
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06/14/2020
General-Purpose Differentially-Private Confidence Intervals
One of the most common statistical goals is to estimate a population par...
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04/24/2020
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological...
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10/29/2019
Differentially Private Bayesian Linear Regression
Linear regression is an important tool across many fields that work with...
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06/24/2019
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Recent work in variational inference (VI) uses ideas from Monte Carlo es...
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04/16/2019
A Bayesian Perspective on the Deep Image Prior
The deep image prior was recently introduced as a prior for natural imag...
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01/26/2019
Graphical-model based estimation and inference for differential privacy
Many privacy mechanisms reveal high-level information about a data distr...
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09/06/2018
Differentially Private Bayesian Inference for Exponential Families
The study of private inference has been sparked by growing concern regar...
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08/27/2018
Importance Weighting and Variational Inference
Recent work used importance sampling ideas for better variational bounds...
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08/27/2018
Importance Weighting and Varational Inference
Recent work used importance sampling ideas for better variational bounds...
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06/08/2018
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
We develop nested automatic differentiation (AD) algorithms for exact in...
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06/14/2017
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
We investigate the problem of learning discrete, undirected graphical mo...
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12/01/2016
Robust Optimization for Tree-Structured Stochastic Network Design
Stochastic network design is a general framework for optimizing network ...
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04/14/2016
Consistently Estimating Markov Chains with Noisy Aggregate Data
We address the problem of estimating the parameters of a time-homogeneou...
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03/04/2015
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by...
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05/20/2014