Daniel R. Sheldon

2017

Exact inference for integer latent-variable models by Kevin Winner, Debora Sujono, and Daniel Sheldon. Proceedings of International Conference on Machine Learning (ICML), 2017.
Differentially Private Learning of Undirected Graphical Models Using Collective Graphical Models by Garrett, Bernstein, Ryan McKenna, Tao Sun, Daniel Sheldon, Michael Hay, and Gerome Miklau. Proceedings of International Conference on Machine Learning (ICML), 2017.
Kálmán filters for continuous-time movement models by Christen H. Fleming, Daniel Sheldon, Eliezer Gurarie, William F. Fagan, Scott LaPoint, and Justin M. Calabrese. Ecological Informatics, Volume 40, 2017, Pages 8–21.
Robust Optimization for Tree-Structured Stochastic Network Design by Xiaojian Wu, Akshat Kumar, Daniel Sheldon, and Shlomo Zilberstein. AAAI Conference on Artificial Intelligence (AAAI), 2017. Best Paper Award, Computational Sustainability Track

2016

Probabilistic inference with generating functions for Poisson latent variable models by Kevin Winner and Daniel Sheldon. Neural Information Processing Systems (NIPS), 2016
Distinguishing Weather Phenomena from Bird Migration Patterns in Radar Imagery by Aruni RoyChowdhury, Daniel Sheldon, Subhransu Maji and Erik Learned-Miller. CVPR workshop on Perception Beyond the Visual Spectrum (PBVS), 2016.
Innovative visualizations shed light on avian nocturnal migration by Judy Shamoun-Baranes, Andrew Farnsworth, Bart Aelterman, Jose A. Alves, Kevin Azijn, Garrett Bernstein, Sergio Branco, Peter Desmet, Adriaan M. Dokter, Kyle Horton, Steve Kelling, Jeffrey F. Kelly, Hidde Leijnse, Jingjing Rong, Daniel Sheldon, Wouter Van den Broeck, Jan Klaas Van Den Meersche, Benjamin Mark Van Doren, and Hans van Gasteren, PLoS ONE, 11(8):1-15, 2016
The implications of mid-latitude climate extremes for North American migratory bird populations by Frank A. La Sorte, Wesley M. Hochachka, Andrew Farnsworth, André A. Dhondt, and Daniel Sheldon.
Quantifying non-breeding season occupancy patterns and the timing and drivers of autumn migration for a migratory songbird using Doppler radar by Andrew J. Laughlin, Daniel R. Sheldon, David W. Winkler andand Caz M. Taylor. Ecography 39: 001–008, 2016.
Consistently Estimating Markov Chains with Noisy Aggregate Data by Garrett Bernstein and Daniel Sheldon. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
Approximate Inference Using DC Programming For Collective Graphical Models by Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, and Daniel Sheldon. International Conference on Artificial Intelligence and Statistics (AISTATS), 2016.
Robust Decision Making For Stochastic Network Design by Akshat Kumar, Arambam James Singh, Pradeep Varakantham, Daniel Sheldon. AAAI Conference on Artificial Intelligence (AAAI), 2016.
Optimizing Resilience in Large Scale Networks by Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein. AAAI Conference on Artificial Intelligence (AAAI), 2016.

2015

Seasonal changes in the altitudinal distribution of nocturnally migrating birds during autumn migration by Frank A. La Sorte, Wesley M. Hochachka, Andrew Farnsworth, Daniel Sheldon, Benjamin M. Van Doren, Daniel Fink, Steve Kelling. Royal Society Open Science, 2015. 2:150347, 2015.
A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity by Eldar Rakhimberdiev, David W. Winkler, Eli Bridge, Nathaniel E. Seavy, Daniel Sheldon, Theunis Piersma and Anatoly Saveliev. Movement Ecology 3:25, 2015.
A characterization of autumn nocturnal migration detected by weather surveillance radars in the northeastern US by Andrew Farnsworth, Benjamin Mark Van Doren, Wesley M. Hochachka, Daniel Sheldon, Kevin Winner, Jed Irvine, Jeffrey Geevarghese, and Steve Kelling. Ecological Applications. In press.
An Optimization Framework for Merging Multiple Result Lists by Chia-Jung Lee, Qingyao Ai, W. Bruce Croft and Daniel Sheldon. In Proceedings of the 24th ACM Conference on Information and Knowledge Management (CIKM 2015). To appear.
A Fast Combinatorial Algorithm for Optimizing the Spread of Cascades by Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, 2015.
Bethe projections for non-local inference by Luke Vilnis, David Belanger, Daniel Sheldon, and Andrew McCallum. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2015
Message Passing for Collective Graphical Models. by Tao Sun, Daniel Sheldon, and Akshat Kumar. In Proceedings of the 32nd International Conference on Machine Learning (ICML) 2015.
Migration timing and its determinants for nocturnal migratory birds during autumn migration by Frank La Sorte, Wesley Hochachka, Andrew Farnsworth, Daniel Sheldon, Daniel Fink, Jeffrey Geevarghese, Kevin Winner, Benjamin Van Doren, and Steve Kelling.Journal of Animal Ecology, 2015.
Inference in a partially observed queueing model with applications in ecology. by Kevin Winner, Garrett Bernstein, and Daniel Sheldon, In Proceedings of the 32nd International Conference on Machine Learning (ICML) 2015. [supplementary material]
Scheduling conservation designs for maximum flexibility via network cascade optimization by Shan Xue, Alan Fern, and Daniel Sheldon. Journal of Artificial Intelligence Research, 52:331–360, 2015
Inference in a partially observed queueing model with applications in ecology by Kevin Winner and Daniel Sheldon. AAAI 2015 Workshop on Computational Sustainability, 2015

2014

Stochastic network design in bidirected trees by Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Neural Information Processing Systems (NIPS), 2014.
Autumn morning flights of migrant songbirds in the northeastern United States are linked to nocturnal migration and winds aloft by Benjamin M. Van Doren, Daniel Sheldon, Jeffrey Geevarghese, Wesley M. Hochachka, and Andrew Farnsworth. The Auk 132 (1), 105-118.
Reconstructing Velocities of Migrating Birds from Weather Radar—A Case Study in Computational Sustainability by Andrew Farnsworth, Daniel Sheldon, Jeffrey Geevarghese, Jed Irvine, Benjamin Van Doren, Kevin Webb, Thomas G. Dietterich, and Steve Kelling, AI Magazine, Vol. 35 No. 2, pp.31–48, 2014.
Rounded Dynamic Programming for Tree-Structured Stochastic Network Design by Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. In Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), 2014.
Gaussian Approximation of Collective Graphical Models by Liping Liu, Daniel Sheldon, and Thomas G. Dietterich. In Proceedings of the 31st International Conference on Machine Learning (ICML), 2014.
Behavioral drivers of communal roosting in a songbird: a combined theoretical and empirical approach by Andrew J. Laughlin, Daniel R. Sheldon, David W. Winkler, and Caz M. Taylor, Behavioral Ecology, Mar. 2014.
Dynamic Resource Allocation for Optimizing Population Diffusion by Shan Xue, Alan Fern, and Daniel Sheldon. AISTATS, 2014.

2013

Marginal Inference in MRFs using Frank-Wolfe by David Belanger, Daniel Sheldon, and Andrew McCallum. NIPS 2013 Workshop on Greedy Optimization, Frank-Wolfe and Friends.
Stochastic Network Design for River Networks by Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. NIPS Workshop on Machine Learning for Sustainability (MLSUST), 2013.
Message Passing for Collective Graphical Models by Tao Sun, Daniel Sheldon, and Akshat Kumar. NIPS Workshop on Machine Learning for Sustainability (MLSUST), 2013.
Dynamic Resource Allocation for Optimizing Population Diffusion by Shan Xue, Alan Fern, and Daniel Sheldon. NIPS Workshop on Machine Learning for Sustainability (MLSUST), 2013.
Collective Diffusion Over Networks: Models and Inference by Akshat Kumar, Daniel Sheldon and Biplav Srivastava. In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013.
Approximate Inference in Collective Graphical Models by Daniel Sheldon, Tao Sun, Akshat Kumar, and Thomas G. Dietterich. In Proceedings of the 30th International Conference on Machine Learning (ICML), 2013.
Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar by Daniel Sheldon, Andrew Farnsworth, Jed Irvine, Benjamin Van Doren, Kevin Webb, Thomas G. Dietterich, and Steve Kelling. In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), 2013. Best Paper Award, Computational Sustainability Track
Parameter Learning for Latent Network Diffusion by Xiaojian Wu, Akshat Kumar, Daniel Sheldon, and Shlomo Zilberstein, In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013.
Discrete Adaptive Rejection Sampling by Daniel Sheldon, Technical Report UM-CS-2013-012, School of Computer Science, University of Massachusetts, Amherst, Massachusetts, May 2013. [code]
Integrating Information from Geolocators, Weather Radar and Citizen Science to Uncover a Key Stopover Area for an Aerial Insectivore by Andrew J. Laughlin, Caz M. Taylor, David W. bradley, Dayna Leclair, Robert G. Clark, Russell D. Dawson , Peter O. Dunn, Andrew Horn, Marty Leonard, Daniel R. Sheldon, Dave Shutler, Linda A. Whittingham, David W. Winkler, and D. Ryan Norris. The Auk, 130(2):230–239, 2013.
Hamming Approximation of NP Witnesses by Daniel Sheldon and Neal E. Young. Theory of Computing, 9(22):685–702, 2013.

2012

Machine Learning for Computational Sustainability by Thomas G. Dietterich, Ethan Dereszynski, Rebecca A. Hutchinson, and Daniel Sheldon. International Conference on Green Computing (IGCC-2012), 2012. Full open access version without IEEE copyright.
Scheduling Conservation Designs via Network Cascade Optimization by Shan Xue, Alan Fern, and Daniel Sheldon. Twenty-Sixth Conference on Artificial Intelligence (AAAI-12), 2012.
Elevated Summer Temperatures Delay Spawning and Reduce Redd Construction for Resident Brook Trout (Salvelinus fontinalis) by Dana R. Warren, Jason M. Robinson, Daniel C. Josephson, Daniel R. Sheldon and Clifford E. Kraft. Global Change Biology, In press, 2012.
First Passage Time of Skew Brownian Motion by Thilanka Appuhamilage and Daniel Sheldon. arXiv:1008.2989, to appear in Journal of Applied Probability, 2012.
Data Intensive Science Applied to Broad-Scale Citizen Science by Wesley M. Hochachka, Daniel Fink, Rebecca A. Hutchinson, Daniel Sheldon, Weng-Keen Wong and Steve Kelling. Trends in Ecology & Evolution, 2012. http://dx.doi.org/10.1016/j.tree.2011.11.006.

2011

Collective Graphical Models by Daniel Sheldon and Thomas G. Dietterich. Neural Information Processing Systems (NIPS), 2011.
LambdaMerge: Merging the Results of Query Reformulations by Daniel Sheldon, Milad Shokouhi, Martin Szummer and Nick Craswell. Fourth ACM Conference on Web Search and Data Mining (WSDM) 2011.

2010

Maximizing the Spread of Cascades Using Network Design by Daniel Sheldon, Bistra Dilkina, Adam Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla Gomes, David Shmoys, Will Allen, Ole Amundsen and Buck Vaughan, Conference on Uncertainty in Artificial Intelligence (UAI), 2010.
Spatiotemoral Exploratory Models for Broad-scale Survey Data by Daniel Fink, Wesley M. Hochachka, Benjamin Zuckerberg, David W. Winkler, Ben Shaby, M. Arthur Munson, Giles Hooker, Mirek Riedewald, Daniel Sheldon and Steve Kelling, Ecological Applications, 2010.
A Method for Measuring the Relative Information Content of Data from Different Monitoring Protocols by M. Arthur Munson, Rich Caruana, Daniel Fink, Wesley M. Hochachka, Marshall Iliff, Kenneth V. Rosenberg, Daniel Sheldon, Brian L. Sullivan, Christopher Wood and Steve Kelling. Methods in Ecology and Evolution, 2010.

2009

Manipulation of PageRank and Collective Hidden Markov Models by Daniel Sheldon, Ph.D. thesis, Cornell University, 2009.
Manipulation-Resistant Reputations Using Hitting Time (Extended Journal Version) by John Hopcroft and Daniel Sheldon, Internet Mathematics, 5(1), pp. 71-90, 2009.
The eBird Reference Dataset by M. Arthur Munson, Kevin Webb, Daniel Sheldon, Daniel Fink, Wesley M. Hochachka, Marshall Iliff, Mirek Riedewald, Daria Sorokina, Brian Sullivan, Christopher Wood, and Steve Kelling. Cornell Lab of Ornithology and National Audubon Society, Ithaca, NY, June 2009.

2008

Graphical Multi-Task Learning by Daniel Sheldon. NIPS Workshop on Structured Input and Structured Output, 2008. [Extended technical report].
Network Reputation Games by John Hopcroft and Daniel Sheldon. Cornell University Technical Report http://hdl.handle.net/1813/11579, 2008.

2007 and earlier

Collective Inference on Markov Models for Modeling Bird Migration by Daniel R. Sheldon, M.A. Saleh Elmohamed, Dexter Kozen, Neural Information Processing Systems (NIPS), 2007.
Manipulation-Resistant Reputations Using Hitting Time by John Hopcroft and Daniel Sheldon, Workshop and Algorithms and Models for the Web Graph (WAW), 2007.
Green's Functions on Fractals by Jun Kigami, Daniel Sheldon and Robert Strichartz. Fractals, 2000.

Abstracts

Inferring moth emergence from abundance data: A novel mathematical approach using birth-death contingency tables by Daniel Sheldon, Evan Goldman, Erin Childs, Olivia Poblacion, Jefftey C. Miller, Julia A. Jones and Thomas G. Dietterich. Ecological Society of America Annual Meeting: Session on Ecological Applications of Machine Learning, 2011.