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Found 30 papers, 10 papers with code
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data
This technique allows for public data to be included in a graphical-model-based mechanism.
Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling
Human feedback on the pairwise similarity can be used to improve the clustering, but existing approaches do not guarantee accurate count estimates.
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling
Many modern applications use computer vision to detect and count objects in massive image collections.
Kernel Interpolation with Sparse Grids
Structured kernel interpolation (SKI) accelerates Gaussian process (GP) inference by interpolating the kernel covariance function using a dense grid of inducing points, whose corresponding kernel matrix is highly structured and thus amenable to fast linear algebra.
Sample Average Approximation for Black-Box VI
We present a novel approach for black-box VI that bypasses the difficulties of stochastic gradient ascent, including the task of selecting step-sizes.
U-Statistics for Importance-Weighted Variational Inference
We propose the use of U-statistics to reduce variance for gradient estimation in importance-weighted variational inference.
Automatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models.
Variational Marginal Particle Filters
We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability.
Relaxed Marginal Consistency for Differentially Private Query Answering
Private-PGM is a recent approach that uses graphical models to represent the data distribution, with complexity proportional to that of exact marginal inference in a graphical model with structure determined by the co-occurrence of variables in the noisy measurements.
Sibling Regression for Generalized Linear Models
Field observations form the basis of many scientific studies, especially in ecological and social sciences.
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data
In particular, we model the aligned data as a spatio-temporal Poisson point process.
Faster Kernel Interpolation for Gaussian Processes
Structured kernel interpolation (SKI) is among the most scalable methods: by placing inducing points on a dense grid and using structured matrix algebra, SKI achieves per-iteration time of O(n + m log m) for approximate inference.
Three-quarter Sibling Regression for Denoising Observational Data
However, it will remove intrinsic variability if the variables are dependent, and therefore does not apply to many situations, including modeling of species counts that are controlled by common causes.
Normalizing Flows Across Dimensions
In this paper we introduce noisy injective flows (NIF), a generalization of normalizing flows that can go across dimensions.
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization
The combination of these algorithmic components significantly advances the state-of-the-art "out of the box" variational inference.
Parametric Bootstrap for Differentially Private Confidence Intervals
The goal of this paper is to develop a practical and general-purpose approach to construct confidence intervals for differentially private parametric estimation.
Detecting and Tracking Communal Bird Roosts in Weather Radar Data
The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years.
Differentially Private Bayesian Linear Regression
Linear regression is an important tool across many fields that work with sensitive human-sourced data.
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation
Recent work in variational inference (VI) uses ideas from Monte Carlo estimation to tighten the lower bounds on the log-likelihood that are used as objectives.
A Bayesian Perspective on the Deep Image Prior
The deep image prior was recently introduced as a prior for natural images.
Graphical-model based estimation and inference for differential privacy
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements.
Differentially Private Bayesian Inference for Exponential Families
The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources.
Importance Weighting and Variational Inference
Recent work used importance sampling ideas for better variational bounds on likelihoods.
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models.
Differentially Private Learning of Graphical Models using CGMs
A naive learning algorithm that uses the noisy sufficient statistics “as is” outperforms general-purpose differentially private learning algorithms.
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models
We investigate the problem of learning discrete, undirected graphical models in a differentially private way.
Robust Optimization for Tree-Structured Stochastic Network Design
We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers.
Consistently Estimating Markov Chains with Noisy Aggregate Data
We develop a new, simpler method of moments estimator that bypasses this problem and is consistent under noisy observations.
Bethe Projections for Non-Local Inference
Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives.
Gaussian Approximation of Collective Graphical Models
The Collective Graphical Model (CGM) models a population of independent and identically distributed individuals when only collective statistics (i. e., counts of individuals) are observed.
