Daniel Sheldon

Orcid: 0000-0002-4257-2432

Affiliations:
  • University of Massachusetts Amherst, College of Information and Computer Sciences, MA, USA


According to our database1, Daniel Sheldon authored at least 73 papers between 2007 and 2024.

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Bibliography

2024
Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
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PLoS Comput. Biol., 2024

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models.
CoRR, 2024

Efficient and Private Marginal Reconstruction with Local Non-Negativity.
CoRR, 2024

Private Regression via Data-Dependent Sufficient Statistic Perturbation.
CoRR, 2024

Human-in-the-Loop Visual Re-ID for Population Size Estimation.
Proceedings of the Computer Vision - ECCV 2024, 2024

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
U-Statistics for Importance-Weighted Variational Inference.
Trans. Mach. Learn. Res., 2023

Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling.
CoRR, 2023

Sample Average Approximation for Black-Box VI.
CoRR, 2023

Automatically marginalized MCMC in probabilistic programming.
Proceedings of the International Conference on Machine Learning, 2023

2022
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data.
Proc. VLDB Endow., 2022

Kernel Interpolation with Sparse Grids.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Variational Marginal Particle Filters.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Parametric Bootstrap for Differentially Private Confidence Intervals.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data.
J. Priv. Confidentiality, 2021

AI for conservation: learning to track birds with radar.
XRDS, 2021

Sibling Regression for Generalized Linear Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Relaxed Marginal Consistency for Differentially Private Query Answering.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Faster Kernel Interpolation for Gaussian Processes.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Normalizing Flows Across Dimensions.
CoRR, 2020

General-Purpose Differentially-Private Confidence Intervals.
CoRR, 2020

Permute-and-Flip: A new mechanism for differentially private selection.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Detecting and Tracking Communal Bird Roosts in Weather Radar Data.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Computational sustainability: computing for a better world and a sustainable future.
Commun. ACM, 2019

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Differentially Private Bayesian Linear Regression.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Three-quarter Sibling Regression for Denoising Observational Data.
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Graphical-model based estimation and inference for differential privacy.
Proceedings of the 36th International Conference on Machine Learning, 2019

A Bayesian Perspective on the Deep Image Prior.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018
Importance Weighting and Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Differentially Private Bayesian Inference for Exponential Families.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning in Integer Latent Variable Models with Nested Automatic Differentiation.
Proceedings of the 35th International Conference on Machine Learning, 2018

SolarClique: Detecting Anomalies in Residential Solar Arrays.
Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, 2018

2017
Kálmán filters for continuous-time movement models.
Ecol. Informatics, 2017

Exact Inference for Integer Latent-Variable Models.
Proceedings of the 34th International Conference on Machine Learning, 2017

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models.
Proceedings of the 34th International Conference on Machine Learning, 2017

A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election.
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Robust Optimization for Tree-Structured Stochastic Network Design.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery.
Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2016

Approximate Inference Using DC Programming For Collective Graphical Models.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Consistently Estimating Markov Chains with Noisy Aggregate Data.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Optimizing Resilience in Large Scale Networks.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

Robust Decision Making for Stochastic Network Design.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization.
J. Artif. Intell. Res., 2015

Bethe Projections for Non-Local Inference.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Fast Combinatorial Algorithm for Optimizing the Spread of Cascades.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Inference in a Partially Observed Queuing Model with Applications in Ecology.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Message Passing for Collective Graphical Models.
Proceedings of the 32nd International Conference on Machine Learning, 2015

An Optimization Framework for Merging Multiple Result Lists.
Proceedings of the 24th ACM International Conference on Information and Knowledge Management, 2015

2014
Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability.
AI Mag., 2014

Stochastic Network Design in Bidirected Trees.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Gaussian Approximation of Collective Graphical Models.
Proceedings of the 31th International Conference on Machine Learning, 2014

Dynamic Resource Allocation for Optimizing Population Diffusion.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Rounded Dynamic Programming for Tree-Structured Stochastic Network Design.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Hamming Approximation of NP Witnesses.
Theory Comput., 2013

Collective Diffusion Over Networks: Models and Inference.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Parameter Learning for Latent Network Diffusion.
Proceedings of the IJCAI 2013, 2013

Approximate Inference in Collective Graphical Models.
Proceedings of the 30th International Conference on Machine Learning, 2013

Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

2012
First Passage Time of Skew Brownian Motion.
J. Appl. Probab., 2012

Machine learning for computational sustainability.
Proceedings of the 2012 International Green Computing Conference, 2012

Scheduling Conservation Designs via Network Cascade Optimization.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
LambdaMerge: merging the results of query reformulations.
Proceedings of the Forth International Conference on Web Search and Web Data Mining, 2011

Collective Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010
Manipulation of PageRank and Collective Hidden Markov Models.
PhD thesis, 2010

Maximizing the Spread of Cascades Using Network Design.
Proceedings of the UAI 2010, 2010

2008
Manipulation-Resistant Reputations Using Hitting Time.
Internet Math., 2008

2007
Collective Inference on Markov Models for Modeling Bird Migration.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007


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