Adaptive submodularity: A new approach to active learning and stochastic optimization

COLT 2010 - The 23rd Conference on Learning Theory

Volumn , Issue , 2010, Pages 333-345

  Golovin, Daniel ^a   Krause, Andreas ^a  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIVERSE APPLICATIONS; NATURAL GENERALIZATION; OPTIMAL POLICIES; PARTIAL OBSERVABILITY; SENSOR PLACEMENT; STOCHASTIC OPTIMIZATION PROBLEMS; STOCHASTIC OPTIMIZATIONS; SUBMODULAR SET FUNCTIONS;

OPTIMIZATION; STOCHASTIC SYSTEMS;

PROBLEM SOLVING;

EID: 77956509565     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. Arkin, E. M., Meijer, H., Mitchell, J. S. B., Rappaport, D., & Skiena, S. S. (1993). Decision trees for geometric models. Proc. of Symposium on Computational Geometry (pp. 369-378). New York, NY, USA: ACM.
2. Asadpour, A., Nazerzadeh, H., & Saberi, A. (2008). Stochastic submodular maximization. WINE '08: Proc. of the 4th International Workshop on Internet and Network Economics (pp. 477-489). Berlin, Heidelberg: Springer-Verlag.
3. Chakaravarthy, V. T., Pandit, V., Roy, S., Awasthi, P., & Mohania, M. (2007). Decision trees for entity identification: Approximation algorithms and hardness results. In Proceedings of the ACM-SIGMOD Symposium on Principles of Database Systems.
4. Dasgupta, S. (2004). Analysis of a greedy active learning strategy. NIPS: Advances in Neural Information Processing Systems (pp. 337-344). MIT Press.
5. Feige, U. (1998). A threshold of In n for approximating set cover. Journal of the ACM, 45, 634-652.
6. Feige, U., & Lund, C. (1997). On the hardness of computing the permanent of random matrices. Computational Complexity, 6, 101-132.
7. Fujishige, S. (1991). Submodular functions and optimization. No. 47. North Holland, Amsterdam: Annals of Discrete. Mathematics. 2nd edition.
8. Garey, M. R., & Graham, R. L. (1974). Performance bounds on the splitting algorithm for binary testing. Acta Inf., 3, 347-355.
9. Goemans, M. X., & Vondrák, J. (2006). Stochastic covering and adaptivity. LATIN (pp. 532-543). Springer.
10. Golovin, D., & Krause, A. (2010). Adaptive submodularity: A new approach to active learning and stochastic optimization. CoRR, abs/1003.3967.
11. Goundan, P. R., & Schulz, A. S. (2007). Revisiting the greedy approach to submodular set function maximization (Technical Report). Massachusetts Institute of Technology.
12. Guillory, A., & Bilmes, J. (2009). Average-case active learning with costs. The 20th International Conference on Algorithmic Learning Theory. University of Porto, Portugal.
13. Guillory, A., & Bilmes, J. (2010). Interactive submodular set cover. To appear in Proc. International Conference on Machine Learning (ICML).
14. Gupta, A., Krishnaswamy, R., Nagarajan, V., & Ravi, R. (2010). Approximation algorithms for optimal decision trees and adaptive tsp problems. To appear in Proc. International Colloquium on Automata, Languages and Programming (ICALP).
15. Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the spread of influence through a social network. KDD '03: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 137-146). New York, NY, USA: ACM.
16. Kosaraju, S. R., and Przytycka., T. M., & Borgstrom, R. S. (1999). On an optimal split tree problem. Proc. of the 6th Intl. Workshop on Algorithms and Data Structures (pp. 157-168). London, UK: Springer-Verlag.
17. Krause, A., & Guestrin, C. (2007). Near-optimal observation selection using submodular functions. AAAI Nectar track (pp. 1650-1654).
18. Loveland, D. W. (1985). Performance bounds for binary testing with arbitrary weights. Acta Inf., 22, 101-114.
19. McCallum, A., & Nigam, K. (1998). Employing EM and pool-based active learning for text classification. ICML (pp. 350-358). Morgan Kaufmann.
20. Minoux, M. (1978). Accelerated greedy algorithms for maximizing submodular set functions. Proc. of the 8th IFIP Conference on Optimization Techniques (pp. 234-243). Springer.
21. Nemhauser, G. L., and Wolsey., L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions - I. Mathematical Programming, 14, 265-294.
22. Nowak, R. (2009). Noisy generalized binary search. In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams and A. Culotta (Eds.), Advances in neural information processing systems 22, 1366-1374.
23. Schrijver, A. (2003). Combinatorial optimization: polyhedra and efficiency. Volume B, Part IV, Chapters 39-49. Springer.