| 2025 | ICCV | Consensus-Driven Active Model Selection. | Justin Kay, Grant Van Horn, Subhransu Maji, Daniel Sheldon, Sara Beery |
| 2024 | AAAI | DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling. | Gustavo Prez, Subhransu Maji, Daniel Sheldon |
| 2024 | AISTATS | Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. | Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon |
| 2024 | ECCV | Human-in-the-Loop Visual Re-ID for Population Size Estimation. | Gustavo Prez, Daniel Sheldon, Grant Van Horn, Subhransu Maji |
| 2024 | UAI | Sample Average Approximation for Black-Box Variational Inference. | Javier Burroni, Justin Domke, Daniel Sheldon |
| 2023 | ICML | Automatically marginalized MCMC in probabilistic programming. | Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon |
| 2022 | AISTATS | Parametric Bootstrap for Differentially Private Confidence Intervals. | Cecilia Ferrando, Shufan Wang, Daniel Sheldon |
| 2022 | AISTATS | Variational Marginal Particle Filters. | Jinlin Lai, Justin Domke, Daniel Sheldon |
| 2021 | AISTATS | Faster Kernel Interpolation for Gaussian Processes. | Mohit Yadav, Daniel Sheldon, Cameron Musco |
| 2021 | ICCV | 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, Pia Bideau |
| 2020 | AAAI | Detecting and Tracking Communal Bird Roosts in Weather Radar Data. | Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler |
| 2019 | CVPR | A Bayesian Perspective on the Deep Image Prior. | Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon |
| 2019 | ICML | Graphical-model based estimation and inference for differential privacy. | Ryan McKenna, Daniel Sheldon, Gerome Miklau |
| 2019 | IJCAI | Three-quarter Sibling Regression for Denoising Observational Data. | Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich |
| 2018 | ICML | Learning in Integer Latent Variable Models with Nested Automatic Differentiation. | Daniel Sheldon, Kevin Winner, Debora Sujono |
| 2017 | AAAI | Robust Optimization for Tree-Structured Stochastic Network Design. | XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein |
| 2017 | ICDM | A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election. | Tao Sun, Daniel Sheldon, Brendan O'Connor |
| 2017 | ICML | Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models. | Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau |
| 2017 | ICML | Exact Inference for Integer Latent-Variable Models. | Kevin Winner, Debora Sujono, Daniel Sheldon |
| 2016 | AAAI | Robust Decision Making for Stochastic Network Design. | Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon |
| 2016 | AAAI | Optimizing Resilience in Large Scale Networks. | XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein |
| 2016 | AISTATS | Consistently Estimating Markov Chains with Noisy Aggregate Data. | Garrett Bernstein, Daniel Sheldon |
| 2016 | AISTATS | Approximate Inference Using DC Programming For Collective Graphical Models. | Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon |
| 2016 | CVPR | Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery. | Aruni Roy Chowdhury, Daniel Sheldon, Subhransu Maji, Erik G. Learned-Miller |
| 2015 | CIKM | An Optimization Framework for Merging Multiple Result Lists. | Chia-Jung Lee, Qingyao Ai, W. Bruce Croft, Daniel Sheldon |
| 2015 | ICML | Message Passing for Collective Graphical Models. | Tao Sun, Daniel Sheldon, Akshat Kumar |
| 2015 | ICML | Inference in a Partially Observed Queuing Model with Applications in Ecology. | Kevin Winner, Garrett Bernstein, Daniel Sheldon |
| 2015 | IJCAI | Fast Combinatorial Algorithm for Optimizing the Spread of Cascades. | XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein |
| 2015 | UAI | Bethe Projections for Non-Local Inference. | Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum |
| 2014 | AAAI | Rounded Dynamic Programming for Tree-Structured Stochastic Network Design. | XiaoJian Wu, Daniel Sheldon, Shlomo Zilberstein |
| 2014 | AISTATS | Dynamic Resource Allocation for Optimizing Population Diffusion. | Shan Xue, Alan Fern, Daniel Sheldon |
| 2014 | ICML | Gaussian Approximation of Collective Graphical Models. | Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich |
| 2013 | AAAI | 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 |
| 2013 | ICML | Approximate Inference in Collective Graphical Models. | Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich |
| 2013 | IJCAI | Parameter Learning for Latent Network Diffusion. | XiaoJian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein |
| 2013 | UAI | Collective Diffusion Over Networks: Models and Inference. | Akshat Kumar, Daniel Sheldon, Biplav Srivastava |
| 2012 | AAAI | Scheduling Conservation Designs via Network Cascade Optimization. | Shan Xue, Alan Fern, Daniel Sheldon |
| 2011 | WSDM | LambdaMerge: merging the results of query reformulations. | Daniel Sheldon, Milad Shokouhi, Martin Szummer, Nick Craswell |
| 2010 | UAI | 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 |
| 2007 | WAW | Manipulation-Resistant Reputations Using Hitting Time. | John E. Hopcroft, Daniel Sheldon |