Daniel Sheldon

108 papers A* 30A 13C 2Journal 54Unranked 8
YearRankTypeTitle / Venue / Authors
2026 J jnl
Fast Private Adaptive Query Answering for Large Data Domains.
CoRR
Miguel Fuentes, Brett Mullins, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel Sheldon
2025 J jnl
Active Measurement: Efficient Estimation at Scale.
CoRR
Max Hamilton, Jinlin Lai, Wenlong Zhao, Subhransu Maji, Daniel Sheldon
2025 J jnl
Consensus-Driven Active Model Selection.
CoRR
Justin Kay, Grant Van Horn, Subhransu Maji, Daniel Sheldon, Sara Beery
2025 J jnl
Private Regression via Data-Dependent Sufficient Statistic Perturbation.
Trans. Mach. Learn. Res.
Cecilia Ferrando, Daniel Sheldon
2024 J jnl
Challenges of COVID-19 Case Forecasting in the US, 2020-2021.
PLoS Comput. Biol.
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, Jade Xiao, Johannes Bracher, Alvaro J. Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H. Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L. Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W. Zorn, Sen Pei, Jeffrey Shaman, Teresa K. Yamana, Samuel R. Tarasewicz, Daniel J. Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L. Hill, Joshua Kaminsky, Elizabeth C. Lee, Joseph Chadi Lemaitre, Justin Lessler, Claire P. Smith, Shaun Truelove, Matt Kinsey, Luke C. Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren A. Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan M. Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T. Davis, Kunpeng Mu, Ana L. Pastore y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav V. Marathe, Akhil Sai Peddireddy, Przemyslaw J. Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V. Prasad, Jo W. Walker, Alexander E. Webber, Rachel B. Slayton, Matthew Biggerstaff, Nicholas G. Reich, Michael A. Johansson
2024 A* conf
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling.
AAAI
Gustavo Pérez, Subhransu Maji, Daniel Sheldon
2024 J jnl
Efficient and Private Marginal Reconstruction with Local Non-Negativity.
CoRR
Brett Mullins, Miguel Fuentes, Yingtai Xiao, Daniel Kifer, Cameron Musco, Daniel Sheldon
2024 J jnl
Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models.
CoRR
Jinlin Lai, Daniel Sheldon, Justin Domke
2024 — conf
Human-in-the-Loop Visual Re-ID for Population Size Estimation.
ECCV (38)
Gustavo Pérez, Daniel Sheldon, Grant Van Horn, Subhransu Maji
2024 A conf
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data.
AISTATS
Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
2024 J jnl
Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data.
CoRR
Miguel Fuentes, Brett Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
2024 J jnl
Private Regression via Data-Dependent Sufficient Statistic Perturbation.
CoRR
Cecilia Ferrando, Daniel Sheldon
2024 A conf
Sample Average Approximation for Black-Box Variational Inference.
UAI
Javier Burroni, Justin Domke, Daniel Sheldon
2023 J jnl
Automatically Marginalized MCMC in Probabilistic Programming.
CoRR
Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
2023 A* conf
Automatically marginalized MCMC in probabilistic programming.
ICML
Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
2023 J jnl
DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling.
CoRR
Gustavo Pérez, Subhransu Maji, Daniel Sheldon
2023 J jnl
Human in-the-Loop Estimation of Cluster Count in Datasets via Similarity-Driven Nested Importance Sampling.
CoRR
Gustavo Pérez, Daniel Sheldon, Grant Van Horn, Subhransu Maji
2023 J jnl
Kernel Interpolation with Sparse Grids.
CoRR
Mohit Yadav, Daniel Sheldon, Cameron Musco
2023 J jnl
Sample Average Approximation for Black-Box VI.
CoRR
Javier Burroni, Justin Domke, Daniel Sheldon
2023 J jnl
U-Statistics for Importance-Weighted Variational Inference.
CoRR
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
2023 J jnl
U-Statistics for Importance-Weighted Variational Inference.
Trans. Mach. Learn. Res.
Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
2022 J jnl
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data.
CoRR
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
2022 J jnl
AIM: An Adaptive and Iterative Mechanism for Differentially Private Synthetic Data.
Proc. VLDB Endow.
Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau
2022 A conf
Parametric Bootstrap for Differentially Private Confidence Intervals.
AISTATS
Cecilia Ferrando, Shufan Wang, Daniel Sheldon
2022 A conf
Variational Marginal Particle Filters.
AISTATS
Jinlin Lai, Justin Domke, Daniel Sheldon
2021 J jnl
AI for conservation: learning to track birds with radar.
XRDS
Zezhou Cheng, Subhransu Maji, Daniel Sheldon
2021 A conf
Faster Kernel Interpolation for Gaussian Processes.
AISTATS
Mohit Yadav, Daniel Sheldon, Cameron Musco
2021 J jnl
Faster Kernel Interpolation for Gaussian Processes.
CoRR
Mohit Yadav, Daniel Sheldon, Cameron Musco
2021 A* conf
Relaxed Marginal Consistency for Differentially Private Query Answering.
NeurIPS
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
2021 J jnl
Relaxed Marginal Consistency for Differentially Private Query Answering.
CoRR
Ryan McKenna, Siddhant Pradhan, Daniel Sheldon, Gerome Miklau
2021 — conf
Sibling Regression for Generalized Linear Models.
ECML/PKDD (2)
Shiv Shankar, Daniel Sheldon
2021 J jnl
Sibling Regression for Generalized Linear Models.
CoRR
Shiv Shankar, Daniel Sheldon
2021 A* conf
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data.
ICCV
Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau
2021 J jnl
The Spatio-Temporal Poisson Point Process: A Simple Model for the Alignment of Event Camera Data.
CoRR
Cheng Gu, Erik G. Learned-Miller, Daniel Sheldon, Guillermo Gallego, Pia Bideau
2021 J jnl
Three-quarter Sibling Regression for Denoising Observational Data.
CoRR
Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich
2021 J jnl
Variational Marginal Particle Filters.
CoRR
Jinlin Lai, Daniel Sheldon, Justin Domke
2021 J jnl
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data.
CoRR
Ryan McKenna, Gerome Miklau, Daniel Sheldon
2021 J jnl
Winning the NIST Contest: A scalable and general approach to differentially private synthetic data.
J. Priv. Confidentiality
Ryan McKenna, Gerome Miklau, Daniel Sheldon
2020 A* conf
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization.
NeurIPS
Abhinav Agrawal, Daniel Sheldon, Justin Domke
2020 J jnl
Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization.
CoRR
Abhinav Agrawal, Daniel Sheldon, Justin Domke
2020 A* conf
Detecting and Tracking Communal Bird Roosts in Weather Radar Data.
AAAI
Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
2020 J jnl
Detecting and Tracking Communal Bird Roosts in Weather Radar Data.
CoRR
Zezhou Cheng, Saadia Gabriel, Pankaj Bhambhani, Daniel Sheldon, Subhransu Maji, Andrew Laughlin, David Winkler
2020 J jnl
General-Purpose Differentially-Private Confidence Intervals.
CoRR
Cecilia Ferrando, Shufan Wang, Daniel Sheldon
2020 J jnl
Normalizing Flows Across Dimensions.
CoRR
Edmond Cunningham, Renos Zabounidis, Abhinav Agrawal, Ina Fiterau, Daniel Sheldon
2020 A* conf
Permute-and-Flip: A new mechanism for differentially private selection.
NeurIPS
Ryan McKenna, Daniel Sheldon
2020 J jnl
Permute-and-Flip: A new mechanism for differentially private selection.
CoRR
Ryan McKenna, Daniel Sheldon
2019 A* conf
A Bayesian Perspective on the Deep Image Prior.
CVPR
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon
2019 J jnl
A Bayesian Perspective on the Deep Image Prior.
CoRR
Zezhou Cheng, Matheus Gadelha, Subhransu Maji, Daniel Sheldon
2019 J jnl
Computational sustainability: computing for a better world and a sustainable future.
Commun. ACM
Carla P. Gomes, Thomas G. Dietterich, Christopher Barrett, Jon Conrad, Bistra Dilkina, Stefano Ermon, Fei Fang, Andrew Farnsworth, Alan Fern, Xiaoli Z. Fern, Daniel Fink, Douglas H. Fisher, Alexander Flecker, Daniel Freund, Angela Fuller, John M. Gregoire, John E. Hopcroft, Steve Kelling, J. Zico Kolter, Warren B. Powell, Nicole D. Sintov, John S. Selker, Bart Selman, Daniel Sheldon, David B. Shmoys, Milind Tambe, Weng-Keen Wong, Christopher Wood, Xiaojian Wu, Yexiang Xue, Amulya Yadav, Abdul-Aziz Yakubu, Mary Lou Zeeman
2019 A* conf
Differentially Private Bayesian Linear Regression.
NeurIPS
Garrett Bernstein, Daniel Sheldon
2019 J jnl
Differentially Private Bayesian Linear Regression.
CoRR
Garrett Bernstein, Daniel Sheldon
2019 A* conf
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation.
NeurIPS
Justin Domke, Daniel Sheldon
2019 J jnl
Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation.
CoRR
Justin Domke, Daniel Sheldon
2019 A* conf
Graphical-model based estimation and inference for differential privacy.
ICML
Ryan McKenna, Daniel Sheldon, Gerome Miklau
2019 J jnl
Graphical-model based estimation and inference for differential privacy.
CoRR
Ryan McKenna, Daniel Sheldon, Gerome Miklau
2019 A* conf
Three-quarter Sibling Regression for Denoising Observational Data.
IJCAI
Shiv Shankar, Daniel Sheldon, Tao Sun, John Pickering, Thomas G. Dietterich
2018 A* conf
Differentially Private Bayesian Inference for Exponential Families.
NeurIPS
Garrett Bernstein, Daniel Sheldon
2018 J jnl
Differentially Private Bayesian Inference for Exponential Families.
CoRR
Garrett Bernstein, Daniel Sheldon
2018 A* conf
Importance Weighting and Variational Inference.
NeurIPS
Justin Domke, Daniel Sheldon
2018 J jnl
Importance Weighting and Variational Inference.
CoRR
Justin Domke, Daniel Sheldon
2018 A* conf
Inferring Latent Velocities from Weather Radar Data using Gaussian Processes.
NeurIPS
Rico Angell, Daniel Sheldon
2018 A* conf
Learning in Integer Latent Variable Models with Nested Automatic Differentiation.
ICML
Daniel Sheldon, Kevin Winner, Debora Sujono
2018 J jnl
Learning in Integer Latent Variable Models with Nested Automatic Differentiation.
CoRR
Daniel Sheldon, Kevin Winner, Debora Sujono
2018 C conf
SolarClique: Detecting Anomalies in Residential Solar Arrays.
COMPASS
Srinivasan Iyengar, Stephen Lee, Daniel Sheldon, Prashant J. Shenoy
2017 A* conf
A Probabilistic Approach for Learning with Label Proportions Applied to the US Presidential Election.
ICDM
Tao Sun, Daniel Sheldon, Brendan O'Connor
2017 A* conf
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models.
ICML
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
2017 J jnl
Differentially Private Learning of Undirected Graphical Models using Collective Graphical Models.
CoRR
Garrett Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, Gerome Miklau
2017 A* conf
Exact Inference for Integer Latent-Variable Models.
ICML
Kevin Winner, Debora Sujono, Daniel Sheldon
2017 J jnl
Kálmán filters for continuous-time movement models.
Ecol. Informatics
Christen H. Fleming, Daniel Sheldon, Eliezer Gurarie, William F. Fagan, Scott LaPoint, Justin M. Calabrese
2017 A* conf
Robust Optimization for Tree-Structured Stochastic Network Design.
AAAI
Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
2016 A conf
Approximate Inference Using DC Programming For Collective Graphical Models.
AISTATS
Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon
2016 A conf
Consistently Estimating Markov Chains with Noisy Aggregate Data.
AISTATS
Garrett Bernstein, Daniel Sheldon
2016 J jnl
Consistently Estimating Markov Chains with Noisy Aggregate Data.
CoRR
Garrett Bernstein, Daniel Sheldon
2016 — conf
Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery.
CVPR Workshops
Aruni Roy Chowdhury, Daniel Sheldon, Subhransu Maji, Erik G. Learned-Miller
2016 A* conf
Optimizing Resilience in Large Scale Networks.
AAAI
Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
2016 — conf
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models.
NIPS
Kevin Winner, Daniel Sheldon
2016 A* conf
Robust Decision Making for Stochastic Network Design.
AAAI
Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon
2016 J jnl
Robust Optimization for Tree-Structured Stochastic Network Design.
CoRR
Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
2015 A conf
An Optimization Framework for Merging Multiple Result Lists.
CIKM
Chia-Jung Lee, Qingyao Ai, W. Bruce Croft, Daniel Sheldon
2015 A conf
Bethe Projections for Non-Local Inference.
UAI
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
2015 J jnl
Bethe Projections for Non-Local Inference.
CoRR
Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
2015 — ed.
Computational Sustainability, Papers from the 2015 AAAI Workshop, Austin, Texas, USA, January 26, 2015.
AAAI Workshop: Computational Sustainability
Bistra Dilkina, Stefano Ermon, Rebecca A. Hutchinson, Daniel Sheldon
2015 A* conf
Fast Combinatorial Algorithm for Optimizing the Spread of Cascades.
IJCAI
Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
2015 A* conf
Inference in a Partially Observed Queuing Model with Applications in Ecology.
ICML
Kevin Winner, Garrett Bernstein, Daniel Sheldon
2015 A* conf
Message Passing for Collective Graphical Models.
ICML
Tao Sun, Daniel Sheldon, Akshat Kumar
2015 J jnl
Scheduling Conservation Designs for Maximum Flexibility via Network Cascade Optimization.
J. Artif. Intell. Res.
Shan Xue, Alan Fern, Daniel Sheldon
2014 A conf
Dynamic Resource Allocation for Optimizing Population Diffusion.
AISTATS
Shan Xue, Alan Fern, Daniel Sheldon
2014 A* conf
Gaussian Approximation of Collective Graphical Models.
ICML
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
2014 J jnl
Gaussian Approximation of Collective Graphical Models.
CoRR
Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
2014 J jnl
Reconstructing Velocities of Migrating Birds from Weather Radar - A Case Study in Computational Sustainability.
AI Mag.
Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling
2014 A* conf
Rounded Dynamic Programming for Tree-Structured Stochastic Network Design.
AAAI
Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
2014 — conf
Stochastic Network Design in Bidirected Trees.
NIPS
Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein
2013 A* conf
Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar.
AAAI
Daniel Sheldon, Andrew Farnsworth, Jed Irvine, Benjamin Van Doren, Kevin F. Webb, Thomas G. Dietterich, Steve Kelling
2013 — conf
Approximate Inference in Collective Graphical Models.
ICML (3)
Daniel Sheldon, Tao Sun, Akshat Kumar, Thomas G. Dietterich
2013 A conf
Collective Diffusion Over Networks: Models and Inference.
UAI
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
2013 J jnl
Collective Diffusion Over Networks: Models and Inference.
CoRR
Akshat Kumar, Daniel Sheldon, Biplav Srivastava
2013 J jnl
Hamming Approximation of NP Witnesses.
Theory Comput.
Daniel Sheldon, Neal E. Young
2013 A* conf
Parameter Learning for Latent Network Diffusion.
IJCAI
Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein
2012 J jnl
First Passage Time of Skew Brownian Motion.
J. Appl. Probab.
Thilanka Appuhamillage, Daniel Sheldon
2012 J jnl
Hamming Approximation of NP Witnesses
CoRR
Daniel Sheldon, Neal E. Young
2012 J jnl
Maximizing the Spread of Cascades Using Network Design
CoRR
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
2012 A* conf
Scheduling Conservation Designs via Network Cascade Optimization.
AAAI
Shan Xue, Alan Fern, Daniel Sheldon
2011 — conf
Collective Graphical Models.
NIPS
Daniel Sheldon, Thomas G. Dietterich
2011 A conf
LambdaMerge: merging the results of query reformulations.
WSDM
Daniel Sheldon, Milad Shokouhi, Martin Szummer, Nick Craswell
2010 A conf
Maximizing the Spread of Cascades Using Network Design.
UAI
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
2008 J jnl
Manipulation-Resistant Reputations Using Hitting Time.
Internet Math.
John E. Hopcroft, Daniel Sheldon
2007 — conf
Collective Inference on Markov Models for Modeling Bird Migration.
NIPS
Daniel Sheldon, M. A. Saleh Elmohamed, Dexter Kozen
2007 C conf
Manipulation-Resistant Reputations Using Hitting Time.
WAW
John E. Hopcroft, Daniel Sheldon