Sheldon, Daniel

41 publications

NeurIPS 2025 Active Measurement: Efficient Estimation at Scale Max Hamilton, Jinlin Lai, Wenlong Zhao, Subhransu Maji, Daniel Sheldon

ICCV 2025 Consensus-Driven Active Model Selection Justin Kay, Grant Van Horn, Subhransu Maji, Daniel Sheldon, Sara Beery

TMLR 2025 Private Regression via Data-Dependent Sufficient Statistic Perturbation Cecilia Ferrando, Daniel Sheldon

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

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

NeurIPS 2024 Gaussian Process Bandits for Top-K Recommendations Mohit Yadav, Daniel Sheldon, Cameron Musco

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

ECCV 2024 Human-in-the-Loop Visual Re-ID for Population Size Estimation Gustavo Perez, Daniel Sheldon, Grant Van Horn, Subhransu Maji

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

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

NeurIPSW 2023 A Semi-Automated System to Annotate Communal Roosts in Large-Scale Weather Radar Data Wenlong Zhao, Gustavo Perez, Zezhou Cheng, Maria Carolina Tiburcio Dias Belotti, Yuting Deng, Victoria Simons, Elske K Tielens, Jeffrey Kelly, Kyle Horton, Subhransu Maji, Daniel Sheldon

ICML 2023 Automatically Marginalized MCMC in Probabilistic Programming Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon

TMLR 2023 U-Statistics for Importance-Weighted Variational Inference Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon

AISTATS 2022 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 2021 Faster Kernel Interpolation for Gaussian Processes Mohit Yadav, Daniel Sheldon, Cameron Musco

ECML-PKDD 2021 Sibling Regression for Generalized Linear Models Shiv Shankar, Daniel Sheldon

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

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

ICML 2019 Graphical-Model Based Estimation and Inference for Differential Privacy Ryan Mckenna, Daniel Sheldon, Gerome Miklau

IJCAI 2019 Three-Quarter Sibling Regression for Denoising Observational Data Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich

ICML 2018 Learning in Integer Latent Variable Models with Nested Automatic Differentiation Daniel Sheldon, Kevin Winner, Debora Sujono

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

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

AISTATS 2016 Approximate Inference Using DC Programming for Collective Graphical Models Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

AISTATS 2016 Consistently Estimating Markov Chains with Noisy Aggregate Data Garrett Bernstein, Daniel Sheldon

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

AAAI 2016 Optimizing Resilience in Large Scale Networks XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein

AAAI 2016 Robust Decision Making for Stochastic Network Design Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon

UAI 2015 Bethe Projections for Non-Local Inference Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum

IJCAI 2015 Fast Combinatorial Algorithm for Optimizing the Spread of Cascades XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein

JAIR 2015 Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization Shan Xue, Alan Fern, Daniel Sheldon

AISTATS 2014 Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue, Alan Fern, Daniel Sheldon

ICML 2014 Gaussian Approximation of Collective Graphical Models Liping Liu, Daniel Sheldon, Thomas Dietterich

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

AAAI 2013 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

ICML 2013 Approximate Inference in Collective Graphical Models Daniel Sheldon, Tao Sun, Akshat Kumar, Tom Dietterich

UAI 2013 Collective Diffusion over Networks: Models and Inference Akshat Kumar, Daniel Sheldon, Biplav Srivastava

IJCAI 2013 Parameter Learning for Latent Network Diffusion XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein

AAAI 2012 Scheduling Conservation Designs via Network Cascade Optimization Shan Xue, Alan Fern, Daniel Sheldon

UAI 2010 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