Collective Inference on Markov Models for Modeling Bird Migration. - Daniel Sheldon; M. A. Saleh Elmohamed; Dexter Kozen (NIPS, 2007)

Manipulation-Resistant Reputations Using Hitting Time. - John E. Hopcroft; Daniel Sheldon (Internet Math., 2008)

Maximizing the Spread of Cascades Using Network Design. - Daniel Sheldon; Bistra Dilkina; Adam N. Elmachtoub; Ryan Finseth; Ashish Sabharwal; Jon Conrad; Carla P. Gomes; David B. Shmoys; William Allen; Ole Amundsen; William Vaughan (UAI, 2010)

Manipulation of PageRank and Collective Hidden Markov Models. - Dan Sheldon (2010)

Collective Graphical Models. - Daniel Sheldon; Thomas G. Dietterich (NIPS, 2011)

LambdaMerge: merging the results of query reformulations. - Daniel Sheldon; Milad Shokouhi; Martin Szummer; Nick Craswell (WSDM, 2011)

Scheduling Conservation Designs via Network Cascade Optimization. - Shan Xue; Alan Fern; Daniel Sheldon (AAAI, 2012)

Machine learning for computational sustainability. - Thomas G. Dietterich; Ethan W. Dereszynski; Rebecca A. Hutchinson; Dan Sheldon (IGCC, 2012)

First Passage Time of Skew Brownian Motion. - Thilanka Appuhamillage; Daniel Sheldon (J. Appl. Probab., 2012)

Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar. - Daniel Sheldon; Andrew Farnsworth; Jed Irvine; Benjamin Van Doren; Kevin F. Webb; Thomas G. Dietterich; Steve Kelling (AAAI, 2013)

Approximate Inference in Collective Graphical Models. - Daniel Sheldon; Tao Sun 008; Akshat Kumar; Thomas G. Dietterich (ICML (3), 2013)

Parameter Learning for Latent Network Diffusion. - XiaoJian Wu; Akshat Kumar; Daniel Sheldon; Shlomo Zilberstein (IJCAI, 2013)

Collective Diffusion Over Networks: Models and Inference. - Akshat Kumar; Daniel Sheldon; Biplav Srivastava (UAI, 2013)

Hamming Approximation of NP Witnesses. - Daniel Sheldon; Neal E. Young (Theory Comput., 2013)

Rounded Dynamic Programming for Tree-Structured Stochastic Network Design. - XiaoJian Wu; Daniel Sheldon; Shlomo Zilberstein (AAAI, 2014)

Dynamic Resource Allocation for Optimizing Population Diffusion. - Shan Xue; Alan Fern; Daniel Sheldon (AISTATS, 2014)

Gaussian Approximation of Collective Graphical Models. - Li-Ping Liu 001; Daniel Sheldon; Thomas G. Dietterich (ICML, 2014)

Stochastic Network Design in Bidirected Trees. - XiaoJian Wu; Daniel Sheldon; Shlomo Zilberstein (NIPS, 2014)

Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability. - Andrew Farnsworth; Daniel Sheldon; Jeffrey Geevarghese; Jed Irvine; Benjamin Van Doren; Kevin F. Webb; Thomas G. Dietterich; Steve Kelling (AI Mag., 2014)

An Optimization Framework for Merging Multiple Result Lists. - Chia-Jung Lee; Qingyao Ai; W. Bruce Croft; Daniel Sheldon (CIKM, 2015)

Message Passing for Collective Graphical Models. - Tao Sun 008; Daniel Sheldon; Akshat Kumar (ICML, 2015)

Inference in a Partially Observed Queuing Model with Applications in Ecology. - Kevin Winner; Garrett Bernstein; Daniel Sheldon (ICML, 2015)

Fast Combinatorial Algorithm for Optimizing the Spread of Cascades. - XiaoJian Wu; Daniel Sheldon; Shlomo Zilberstein (IJCAI, 2015)

Bethe Projections for Non-Local Inference. - Luke Vilnis; David Belanger 002; Daniel Sheldon; Andrew McCallum (UAI, 2015)

Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization. - Shan Xue; Alan Fern; Daniel Sheldon (J. Artif. Intell. Res., 2015)

Robust Decision Making for Stochastic Network Design. - Akshat Kumar; Arambam James Singh; Pradeep Varakantham; Daniel Sheldon (AAAI, 2016)

Optimizing Resilience in Large Scale Networks. - XiaoJian Wu; Daniel Sheldon; Shlomo Zilberstein (AAAI, 2016)

Consistently Estimating Markov Chains with Noisy Aggregate Data. - Garrett Bernstein; Daniel Sheldon (AISTATS, 2016)

Approximate Inference Using DC Programming For Collective Graphical Models. - Duc Thien Nguyen; Akshat Kumar; Hoong Chuin Lau; Daniel Sheldon (AISTATS, 2016)

Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery. - Aruni Roy Chowdhury; Daniel Sheldon; Subhransu Maji; Erik G. Learned-Miller (CVPR Workshops, 2016)

Probabilistic Inference with Generating Functions for Poisson Latent Variable Models. - Kevin Winner; Daniel Sheldon (NIPS, 2016)

Robust Optimization for Tree-Structured Stochastic Network Design. - XiaoJian Wu; Akshat Kumar; Daniel Sheldon; Shlomo Zilberstein (AAAI, 2017)

A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election. - Tao Sun 008; Daniel Sheldon; Brendan O'Connor 001 (ICDM, 2017)

Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. - Garrett Bernstein; Ryan McKenna; Tao Sun 008; Daniel Sheldon; Michael Hay; Gerome Miklau (ICML, 2017)

Exact Inference for Integer Latent-Variable Models. - Kevin Winner; Debora Sujono; Daniel Sheldon (ICML, 2017)

Kálmán filters for continuous-time movement models. - Christen H. Fleming; Daniel Sheldon; Eliezer Gurarie; William F. Fagan; Scott LaPoint; Justin M. Calabrese (Ecol. Informatics, 2017)

SolarClique: Detecting Anomalies in Residential Solar Arrays. - Srinivasan Iyengar; Stephen Lee; Daniel Sheldon; Prashant J. Shenoy (COMPASS, 2018)

Learning in Integer Latent Variable Models with Nested Automatic Differentiation. - Daniel Sheldon; Kevin Winner; Debora Sujono (ICML, 2018)

Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. - Rico Angell; Daniel Sheldon (NeurIPS, 2018)

Differentially Private Bayesian Inference for Exponential Families. - Garrett Bernstein; Daniel Sheldon (NeurIPS, 2018)

Importance Weighting and Variational Inference. - Justin Domke; Daniel Sheldon (NeurIPS, 2018)

A Bayesian Perspective on the Deep Image Prior. - Zezhou Cheng; Matheus Gadelha; Subhransu Maji; Daniel Sheldon (CVPR, 2019)

Graphical-model based estimation and inference for differential privacy. - Ryan McKenna; Daniel Sheldon; Gerome Miklau (ICML, 2019)

Three-quarter Sibling Regression for Denoising Observational Data. - Shiv Shankar; Daniel Sheldon; Tao Sun 008; John Pickering; Thomas G. Dietterich (IJCAI, 2019)

Differentially Private Bayesian Linear Regression. - Garrett Bernstein; Daniel Sheldon (NeurIPS, 2019)

Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation. - Justin Domke; Daniel Sheldon (NeurIPS, 2019)

Computational sustainability: computing for a better world and a sustainable future. - Carla P. Gomes; Thomas G. Dietterich; Christopher Barrett; Jon Conrad; Bistra Dilkina; Stefano Ermon; Fei Fang 001; Andrew Farnsworth; Alan Fern; Xiaoli Z. Fern; Daniel Fink 002; Douglas H. Fisher; et al. (Commun. ACM, 2019)

Detecting and Tracking Communal Bird Roosts in Weather Radar Data. - Zezhou Cheng; Saadia Gabriel; Pankaj Bhambhani; Daniel Sheldon; Subhransu Maji; Andrew Laughlin; David Winkler (AAAI, 2020)

Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization. - Abhinav Agrawal 001; Daniel Sheldon; Justin Domke (NeurIPS, 2020)

Permute-and-Flip: A new mechanism for differentially private selection. - Ryan McKenna; Daniel Sheldon (NeurIPS, 2020)

General-Purpose Differentially-Private Confidence Intervals. - Cecilia Ferrando; Shufan Wang; Daniel Sheldon (CoRR, 2020)

Normalizing Flows Across Dimensions. - Edmond Cunningham; Renos Zabounidis; Abhinav Agrawal 001; Ina Fiterau; Daniel Sheldon (CoRR, 2020)

Faster Kernel Interpolation for Gaussian Processes. - Mohit Yadav; Daniel Sheldon; Cameron Musco (AISTATS, 2021)

The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data. - Cheng Gu; Erik G. Learned-Miller; Daniel Sheldon; Guillermo Gallego 002; Pia Bideau (ICCV, 2021)

Relaxed Marginal Consistency for Differentially Private Query Answering. - Ryan McKenna; Siddhant Pradhan; Daniel Sheldon; Gerome Miklau (NeurIPS, 2021)

Sibling Regression for Generalized Linear Models. - Shiv Shankar; Daniel Sheldon (ECML/PKDD (2), 2021)

AI for conservation: learning to track birds with radar. - Zezhou Cheng; Subhransu Maji; Daniel Sheldon (XRDS, 2021)

Winning the NIST Contest: A scalable and general approach to differentially private synthetic data. - Ryan McKenna; Gerome Miklau; Daniel Sheldon (J. Priv. Confidentiality, 2021)

Parametric Bootstrap for Differentially Private Confidence Intervals. - Cecilia Ferrando; Shufan Wang; Daniel Sheldon (AISTATS, 2022)

Variational Marginal Particle Filters. - Jinlin Lai; Justin Domke; Daniel Sheldon (AISTATS, 2022)

Kernel Interpolation with Sparse Grids. - Mohit Yadav; Daniel R. Sheldon; Cameron Musco (NeurIPS, 2022)

AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data. - Ryan McKenna; Brett Mullins; Daniel Sheldon; Gerome Miklau (Proc. VLDB Endow., 2022)

Automatically marginalized MCMC in probabilistic programming. - Jinlin Lai; Javier Burroni; Hui Guan 001; Daniel Sheldon (ICML, 2023)

Sample Average Approximation for Black-Box VI. - Javier Burroni; Justin Domke; Daniel Sheldon (CoRR, 2023)

Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling. - Gustavo Pérez; Daniel Sheldon; Grant Van Horn; Subhransu Maji (CoRR, 2023)

U-Statistics for Importance-Weighted Variational Inference. - Javier Burroni; Kenta Takatsu; Justin Domke; Daniel Sheldon (Trans. Mach. Learn. Res., 2023)

DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. - Gustavo Pérez; Subhransu Maji; Daniel Sheldon (AAAI, 2024)

Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. - Miguel Fuentes; Brett C. Mullins; Ryan McKenna; Gerome Miklau; Daniel Sheldon (AISTATS, 2024)

Human-in-the-Loop Visual Re-ID for Population Size Estimation. - Gustavo Pérez; Daniel Sheldon; Grant Van Horn; Subhransu Maji (ECCV (38), 2024)

Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models. - Jinlin Lai; Justin Domke; Daniel R. Sheldon (NeurIPS, 2024)

Efficient and Private Marginal Reconstruction with Local Non-Negativity. - Brett Mullins; Miguel Fuentes; Yingtai Xiao; Daniel Kifer; Cameron Musco; Daniel R. Sheldon (NeurIPS, 2024)

Gaussian Process Bandits for Top-k Recommendations. - Mohit Yadav; Cameron Musco; Daniel R. Sheldon (NeurIPS, 2024)

Sample Average Approximation for Black-Box Variational Inference. - Javier Burroni; Justin Domke; Daniel Sheldon (UAI, 2024)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021. - Velma K. Lopez; Estee Y. Cramer; Robert Pagano; John M. Drake; Eamon B. O'Dea; Madeline Adee; Turgay Ayer; Jagpreet Chhatwal; Ozden O. Dalgic; Mary A. Ladd; Benjamin P. Linas; Peter P. Mueller; et al. (PLoS Comput. Biol., 2024)

Private Regression via Data-Dependent Sufficient Statistic Perturbation. - Cecilia Ferrando; Daniel Sheldon (Trans. Mach. Learn. Res., 2025)

Active Measurement: Efficient Estimation at Scale. - Max Hamilton; Jinlin Lai; Wenlong Zhao; Subhransu Maji; Daniel Sheldon (CoRR, 2025)

Consensus-Driven Active Model Selection. - Justin Kay; Grant Van Horn; Subhransu Maji; Daniel Sheldon; Sara Beery (CoRR, 2025)