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We study adaptive sensing of Cox point processes, a widely used model from spatial statistics.
Gaussian process modulated cox processes under linear inequality constraints
López-Lopera, A. F., John, S., and Durrande, N. (2019) · 1902
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Some statistical methods connected with series of events
Cox, D. R. (1955) · 1955
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Sequential design of experiments
Chernoff, H. (1959) · 1959
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A numerically stable dual method for solving strictly convex quadratic programs
Goldfarb, D. and Idnani, A. (1983) · 1983
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Estimating weighted integrals of the second-order intensity of a spatial point process
Berman, M. and Diggle, P. (1989) · 1989
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Poisson Processes
Kingman, J. F. C. (1993) · 1993
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Bayesian experimental design: A review
Chaloner, K. and Verdinelli, I. (1995) · 1995
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Log gaussian cox processes
Møller, J., Syversveen, A. R., and Waagepetersen, R. P. (1998) · 1998
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Modeling a poisson forest in variable elevations: a nonparametric bayesian approach
Heikkinen, J. and Arjas, E. (1999) · 1999
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Using the nyström method to speed up kernel machines
Williams, C. K. and Seeger, M. (2001) · 2001
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Using confidence bounds for exploitation-exploration trade-offs
Auer, P. (2002) · 2002
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Statistical inference and simulation for spatial point processes
Moller, J. and Waagepetersen, R. P. (2003) · 2003
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Convex optimization
Boyd, S. and Vandenberghe, L. (2004) · 2004
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Techniques of Variational Analysis
Borwein, J. and Zhu, Q. (2005) · 2005
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Gaussian processes for machine learning, vol. 1
Rasmussen, C. and Williams, C. (2006) · 2006
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Methods of numerical integration
Davis, P. J. and Rabinowitz, P. (2007) · 2007
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Arrival rate approximation by nonnegative cubic splines
Alizadeh, F., Eckstein, J., Noyan, N., and Rudolf, G. (2008) · 2008
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Fixed rank kriging for very large spatial data sets
Cressie, N. and Johannesson, G. (2008) · 2008
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Interior-point methods for optimization
Nemirovski, A. S. and Todd., M. J. (2008) · 2008
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Tractable nonparametric bayesian inference in poisson processes with gaussian process intensities
Adams, R. P., Murray, I., and MacKay, D. J. (2009) · 2009
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Random fields and geometry
Adler, R. J. and Taylor, J. E. (2009) · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S. M., and Seeger, M. (2010) · 2010
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Random point processes in time and space
Snyder, D. L. and Miller, M. I. (2012) · 2012
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Statistical analysis of spatial point patterns
Diggle, P. (2013) · 2013
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Fast bayesian intensity estimation for the permanental process
Walder, C. J. and Bishop, A. N. (2017) · 2017
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Efficient bayesian inference of sigmoidal gaussian cox processes
Donner, C. and Opper, M. (2018) · 2018
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Efficient bayesian computation by proximal markov chain monte carlo: when langevin meets moreau
Durmus, A., Moulines, E., and Pereyra, M. (2018) · 2018
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Log-concave sampling: Metropolis-hastings algorithms are fast!
Dwivedi, R., Chen, Y., Wainwright, M. J., and Yu, B. (2018) · 2018
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Mirrored langevin dynamics
Hsieh, Y.-P., Kavis, A., Rolland, P., and Cevher, V. (2018) · 2018
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Large-scale Cox process inference using variational Fourier features
John, S. and Hensman, J. (2018) · 2018
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Active learning for level set estimation
Gotovos, A., Casati, N., Hitz, G., and Krause, A. (2013) · 2013
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Papp, D. and Alizadeh, F. (2014) · 2014
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Whale counting in satellite and aerial images with deep learning
Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D., and Herrera, F. (2019) · 2019
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No-regret algorithms for capturing events in poisson point processes
Mutný, M. and Krause, A. (2021) · 2021
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Sensing cox processes via posterior sampling and positive bases
Mutný, M. and Krause, A. (2022) · 2022
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