Seminar on Computational Sustainability: Algorithms for Ecology and Conservation
Includes all scheduled readings, plus additional related papers.
CS/Stats Background and Related Papers
- M. D. Hoffman and A. Gelman, The No-U-Turn Sampler : Adaptively Setting Path Lengths, arXiv preprint arXiv:1111.4246, no. 2008, pp. 1–30, 2011.
- R. Neal, MCMC for Using Hamiltonian Dynamics, Handbook of Markov Chain Monte Carlo, 2011.
- A. Berger, V. Pietra, and S. Pietra, A maximum entropy approach to natural language processing, Computational linguistics, no. 1992, 1996.
- D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, 2003, pp. 137–146.
- P. Diaconis and B. Sturmfels, Algebraic algorithms for sampling from conditional distributions, The Annals of statistics, pp. 363–397, 1998.
Data: Sensing, Data Mining, Crowdsourcing
- C. Guestrin, A. Krause, and A. Singh, Near-optimal sensor placements in gaussian processes, Proceedings of the 22nd International conference on Machine Learning (ICML), vol. 1, 2005.
- A. Krause, A. Singh, and C. Guestrin, Near-Optimal Sensor Placements in Gaussian Processes-Theory, Efficient Algorithms and Empirical Studies, The Journal of Machine Learning Research, vol. 9, 2008.
- D. Sheldon, A. Farnsworth, and J. Irvine, Approximate Bayesian Inference for Reconstructing Velocities of Migrating Birds from Weather Radar, In Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI), 2013.
- Y. Xue, B. Dilkina, T. Damoulas, and D. Fink, Improving Your Chances: Boosting Citizen Science Discovery, AAAI Conference on Human Computation and Crowdsourcing, pp. 198-206, 2013.
- D. MacKenzie, J. Nichols, G. Lachman, S. Droege, J. A. Royle, and C. Langtimm, Estimating site occupancy rates when detection probabilities are less than one, Ecology, vol. 83, no. 8, pp. 2248-2255, 2002.
- D. Dail and L. Madsen, Models for estimating abundance from repeated counts of an open metapopulation., Biometrics, vol. 67, no. 2, pp. 577-87, Jun. 2011.
- J. Friedman, Greedy Function Approximation: A Gradient Boosting Machine, The Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001
- J. Yu, W.-K. Wong, and R. A. Hutchinson, Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling, 2010 IEEE International Conference on Data Mining, pp. 1157-1162, Dec. 2010.
- R. Hutchinson, L. Liu, and T. Dietterich, Incorporating Boosted Regression Trees into Ecological Latent Variable Models., AAAI Conference on Artificial Intelligence, 2011.
Species Distribution Modeling
- S. Phillips, M. Dudík, and R. Schapire, A maximum entropy approach to species distribution modeling, Proceedings of the twenty-first International Conference on Machine Learning (ICML), pp. 655–662, 2004.
- S. J. Phillips, R. P. Anderson, and R. E. Schapire, Maximum entropy modelling of species geographic distributions, Ecological Modelling, vol. 190, pp. 231–259, 2006.
- J. Elith, S. J. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. J. Yates, A statistical explanation of MaxEnt for ecologists, Diversity and Distributions, vol. 17, no. 1, pp. 43–57, Jan. 2011.
- I. W. Renner and D. I. Warton, Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology., Biometrics, vol. 69, no. 1, pp. 274–81, Mar. 2013.
- W. Fithian and T. Hastie, Finite-sample equivalence in statistical models for presence-only data, The Annals of Applied Statistics, vol. 7, no. 4, pp. 1917–1939, Dec. 2013.
- J. Elith, C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. McC. M. Overton, A. Townsend Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann, Novel methods improve prediction of species’ distributions from occurrence data, Ecography, vol. 29, no. 2, pp. 129–151, Apr. 2006.
- A. H. Hurlbert and
Z. Liang, Spatiotemporal
variation in avian migration phenology: citizen science reveals
effects of climate change., PloS one, vol. 7, no. 2, p. e31662, Jan. 2012.
- C. Zonneveld, Estimating Death Rates from Transect Counts, Ecological Entomology, vol. 16, pp. 115–121, 1991.
- E. Matechou, E. B. Dennis, S. N. Freeman, and T. Brereton, Monitoring abundance and phenology in ( multivoltine ) butterfly species ; a novel mixture model, 2013.
- S. Ermon, J. Conrad, C. Gomes, and B. Selman, Playing games against nature: optimal policies for renewable resource allocation, in Proc. of The 26th Conference on Uncertainty in Artificial Intelligence, 2010.
- S. Ermon, J. Conrad, C. Gomes, and B. Selman, Risk-sensitive Policies for Sustainable Renewable Resource Allocation, IJCAI 2011
- I. Chades, J. Carwardine, T. Martin, S. Nicol, R. Sabbadin, and
O. Buffet, MOMDPs: A Solution for Modelling Adaptive Management Problems., Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 267–273, 2012.
- I. Chadès, E. McDonald-Madden, M. a McCarthy, B. Wintle,
M. Linkie, and H. P. Possingham, When to stop managing or surveying cryptic threatened
species, Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 37, pp. 13936–40, Sep. 2008.
Landscape Connectivity and Conservation
- B. McRae, B. Dickson, T. Keitt, and V. Shah, Using circuit theory to model connectivity in ecology, evolution, and conservation Ecology, vol. 89, no. 10, pp. 2712–2724, 2008.
- B. H. McRae, S. a Hall, P. Beier, and D. M. Theobald, Where to Restore Ecological Connectivity? Detecting Barriers and Quantifying Restoration Benefits, PloS one, vol. 7, no. 12, p. e52604, Jan. 2012.
- P. G. Doyle and J. L. Snell, Random walks and electric networks, January, 2000.
- R. Le Bras, B. Dilkina, Y. Xue, and C. Gomes, Robust network design for multispecies conservation, AAAI Conference on Artificial Intelligence, pp. 1305–1312, 2013.
- B. Dilkina and C. Gomes, Solving connected subgraph problems in wildlife conservation CPAIOR-10: 7th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization, 2010.
- J. M. Conrad, C. P. Gomes, W.-J. van Hoeve, A. Sabharwal, and J. F. Suter, Wildlife corridors as a connected subgraph problem, Journal of Environmental Economics and Management, vol. 63, no. 1, pp. 1–18, Jan. 2012
- B. Dilkina, K. J. Lai, and C. P. Gomes, Upgrading Shortest Paths in Networks, International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization.
- K. Lai, C. Gomes, and M. Schwartz, The Steiner Multigraph Problem: Wildlife Corridor Design for Multiple Species, AAAI Conference on Artificial Intelligence, 2011.
- B. Dilkina, K. Lai, R. Le Bras, and Y. Xue, Large Landscape Conservation—Synthetic and Real-World Datasets Notes, pp. 1369–1372, 2013. [data]
- D. Sheldon, B. Dilkina, A. Elmachtoub, R. Finseth, A. Sabharwal, J. Conrad, C. Gomes, D. Shmoys, W. Allen, O. Amundsen, and B. Vauguan, Maximizing the Spread of Cascades Using Network Design, in UAI-2010: 26th Conference on Uncertainty in Artificial Intelligence, pp. 517–526.
- S. Xue, A. Fern, and D. Sheldon, Scheduling Conservation Designs via Network Cascade Optimization, AAAI, 2012.
- D. Golovin, A. Krause, B. Gardner, S. J. Converse, and S. Morey, Dynamic Resource Allocation in Conservation Planning, 2011.
Bird Migration and Collective Graphical Models
- D. Sheldon, M. A. S. Elmohamed, and D. Kozen, Collective inference on Markov models for modeling bird migration Advances in Neural Information Processing Systems, vol. 20, pp. 1321–1328.
- D. Sheldon and T. Dietterich, Collective graphical models, Advances in Neural Information Processing Systems, pp. 1–15, 2011.
- D. Sheldon, T. Sun, A. Kumar, and T. Dietterich, Approximate Inference in Collective Graphical Models International Conference on Machine Learning, vol. 28, 2013.