nips nips2007 nips2007-19 nips2007-19-reference knowledge-graph by maker-knowledge-mining
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Author: Brochu Eric, Nando D. Freitas, Abhijeet Ghosh
Abstract: We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an individual in order to find the item that they value highly in as few trials as possible, and exploits quirks of human psychology to minimize time and cognitive burden. To do this, our algorithm maximizes the expected improvement at each query without accurately modelling the entire valuation surface, which would be needlessly expensive. The problem is particularly difficult because the space of choices is infinite. We demonstrate the effectiveness of the new algorithm compared to related active learning methods. We also embed the algorithm within a decision making tool for assisting digital artists in rendering materials. The tool finds the best parameters while minimizing the number of queries. 1
[Ashikhmin and Shirley, 2000] M. Ashikhmin and P. Shirley. An anisotropic phong BRDF model. J. Graph. Tools, 5(2):25–32, 2000. [Chu and Ghahramani, 2005a] W. Chu and Z. Ghahramani. Extensions of Gaussian processes for ranking: semi-supervised and active learning. In Learning to Rank workshop at NIPS-18, 2005. [Chu and Ghahramani, 2005b] W. Chu and Z. Ghahramani. Preference learning with Gaussian processes. In ICML, 2005. [Cohn et al., 1996] D. A. Cohn, Z. Ghahramani, and M. I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129–145, 1996. 7 T arget 1 2 3 4 Figure 4: A shorter-than-average but otherwise typical run of the preference gallery tool. At each (numbered) iteration, the user is provided with two images generated with parameter instances and indicates the one they think most resembles the target image (top-left) they are looking for. The boxed images are the user’s selections at each iteration. [Cooper et al., 2007] S. Cooper, A. Hertzmann, and Z. Popovi´ . Active learning for motion controllers. In c SIGGRAPH, 2007. ´ o ´ ´ o [El˝ , 1978] A. El˝ . The Rating of Chess Players: Past and Present. Arco Publishing, New York, 1978. [Fleming et al., 2001] R. Fleming, R. Dror, and E. Adelson. How do humans determine reflectance properties under unknown illumination? In CVPR Workshop on Identifying Objects Across Variations in Lighting, 2001. [Glickman and Jensen, 2005] M. E. Glickman and S. T. Jensen. Adaptive paired comparison design. Journal of Statistical Planning and Inference, 127:279–293, 2005. [Guestrin et al., 2005] C. Guestrin, A. Krause, and A. P. Singh. Near-optimal sensor placements in Gaussian processes. In Proceedings of the 22nd International Conference on Machine Learning (ICML-05), 2005. [Jones et al., 1993] D. R. Jones, C. D. Perttunen, and B. E. Stuckman. Lipschitzian optimization without the Lipschitz constant. J. Optimization Theory and Apps, 79(1):157–181, 1993. [Jones et al., 1998] D. R. Jones, M. Schonlau, and W. J. Welch. Efficient global optimization of expensive black-box functions. J. Global Optimization, 13(4):455–492, 1998. [Kendall, 1975] M. Kendall. Rank Correlation Methods. Griffin Ltd, 1975. [Kingsley, 2006] D. C. Kingsley. Preference uncertainty, preference refinement and paired comparison choice experiments. Dept. of Economics, University of Colorado, 2006. [Kushner, 1964] H. J. Kushner. A new method of locating the maximum of an arbitrary multipeak curve in the presence of noise. Journal of Basic Engineering, 86:97–106, 1964. [Marks et al., 1997] J. Marks, B. Andalman, P. A. Beardsley, W. Freeman, S. Gibson, J. Hodgins, T. Kang, B. Mirtich, H. Pfister, W. Ruml, K. Ryall, J. Seims, and S. Shieber. Design galleries: A general approach to setting parameters for computer graphics and animation. Computer Graphics, 31, 1997. [McFadden, 2001] D. McFadden. Economic choices. The American Economic Review, 91:351–378, 2001. [Mosteller, 1951] F. Mosteller. Remarks on the method of paired comparisons: I. the least squares solution assuming equal standard deviations and equal correlations. Psychometrika, 16:3–9, 1951. [Ngan et al., 2005] A. Ngan, F. Durand, and W. Matusik. Experimental analysis of BRDF models. In Proceedings of the Eurographics Symposium on Rendering, pages 117–226, 2005. [Ngan et al., 2006] A. Ngan, F. Durand, and W. Matusik. Image-driven navigation of analytical BRDF models. In T. Akenine-M¨ ller and W. Heidrich, editors, Eurographics Symposium on Rendering, 2006. o [Sasena, 2002] M. J. Sasena. Flexibility and Efficiency Enhancement for Constrained Global Design Optimization with Kriging Approximations. PhD thesis, University of Michigan, 2002. [Seo et al., 2000] S. Seo, M. Wallat, T. Graepel, and K. Obermayer. Gaussian process regression: active data selection and test point rejection. In Proceedings of IJCNN 2000, 2000. [Siegel and Castellan, 1988] S. Siegel and N. J. Castellan. Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill, 1988. [Stern, 1990] H. Stern. A continuum of paired comparison models. Biometrika, 77:265–273, 1990. [Thurstone, 1927] L. Thurstone. A law of comparative judgement. Psychological Review, 34:273–286, 1927. [Tong and Koller, 2000] S. Tong and D. Koller. Support vector machine active learning with applications to text classification. In Proc. ICML-00, 2000. 8