Search Results for author: Daniel Sheldon

Found 23 papers, 5 papers with code

Variational Marginal Particle Filters

no code implementations30 Sep 2021 Jinlin Lai, Daniel Sheldon, Justin Domke

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.

Variational Inference

Relaxed Marginal Consistency for Differentially Private Query Answering

no code implementations13 Sep 2021 Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau

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

no code implementations3 Jul 2021 Shiv Shankar, Daniel Sheldon

Field observations form the basis of many scientific studies, especially in ecological and social sciences.

Faster Kernel Interpolation for Gaussian Processes

no code implementations28 Jan 2021 Mohit Yadav, Daniel Sheldon, Cameron Musco

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.

Gaussian Processes

Three-quarter Sibling Regression for Denoising Observational Data

no code implementations31 Dec 2020 Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

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.

Denoising

Normalizing Flows Across Dimensions

no code implementations23 Jun 2020 Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, Daniel Sheldon

In this paper we introduce noisy injective flows (NIF), a generalization of normalizing flows that can go across dimensions.

Parametric Bootstrap for Differentially Private Confidence Intervals

no code implementations14 Jun 2020 Cecilia Ferrando, Shufan Wang, Daniel Sheldon

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

no code implementations24 Apr 2020 Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years.

Differentially Private Bayesian Linear Regression

1 code implementation NeurIPS 2019 Garrett Bernstein, Daniel Sheldon

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

no code implementations NeurIPS 2019 Justin Domke, Daniel Sheldon

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.

Variational Inference

Graphical-model based estimation and inference for differential privacy

2 code implementations26 Jan 2019 Ryan McKenna, Daniel Sheldon, Gerome Miklau

Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements.

Differentially Private Bayesian Inference for Exponential Families

1 code implementation NeurIPS 2018 Garrett Bernstein, Daniel Sheldon

The study of private inference has been sparked by growing concern regarding the analysis of data when it stems from sensitive sources.

Bayesian Inference

Importance Weighting and Variational Inference

no code implementations NeurIPS 2018 Justin Domke, Daniel Sheldon

Recent work used importance sampling ideas for better variational bounds on likelihoods.

Variational Inference

Learning in Integer Latent Variable Models with Nested Automatic Differentiation

no code implementations ICML 2018 Daniel Sheldon, Kevin Winner, Debora Sujono

We develop nested automatic differentiation (AD) algorithms for exact inference and learning in integer latent variable models.

Latent Variable Models

Differentially Private Learning of Graphical Models using CGMs

no code implementations ICML 2017 Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

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

no code implementations14 Jun 2017 Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau

We investigate the problem of learning discrete, undirected graphical models in a differentially private way.

Robust Optimization for Tree-Structured Stochastic Network Design

no code implementations1 Dec 2016 Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein

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

no code implementations14 Apr 2016 Garrett Bernstein, Daniel Sheldon

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

no code implementations4 Mar 2015 Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives.

Handwriting Recognition Structured Prediction +1

Gaussian Approximation of Collective Graphical Models

no code implementations20 May 2014 Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich

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.

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