Algorithm survival analysis

Experimental Methods for the Analysis of Optimization Algorithms

Volumn , Issue , 2010, Pages 161-184

  Gagliolo, Matteo ^a,b   Legrand, Catherine ^c  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^b UNIVERSITY OF LUGANO   (Switzerland)

^c UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 80855152113     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-02538-9_7     Document Type: Chapter

References (57)

1. Aalen O (1978) Nonparametric inference for a family of counting processes. Annals of Statistics 6:701-726.
2. Akritas M (1994) Nearest neighbor estimation of a bivariate distribution under random censoring. Annals of Statistics 22:1299-1327.
3. Auer P, Cesa-Bianchi N, Freund Y, Schapire RE (2003) The nonstochastic multiarmed bandit problem. SIAM Journal on Computing 32(1):48-77.
4. Beran R (1981) Nonparametric regression with randomly censored survival data. Tech. rep., University of California, Berkeley, CA.
5. Bishop CM(1995) Neural networks for pattern recognition. Oxford University Press.
6. Boddy M, Dean TL (1994) Deliberation scheduling for problem solving in timeconstrained environments. Artificial Intelligence 67(2):245-285.
7. Collet D (2003) Modeling survival data in medical research. Chapman & Hall/CRC, Boca Raton.
8. Cox D (1972) Regression models and life-tables. Journal of the Royal Statistics Society, 34:187-220.
9. Cox D, Oakes D (1984) Analysis of survival data. Chapman & Hall, London.
10. Finkelstein L, Markovitch S, Rivlin E (2002) Optimal schedules for parallelizing anytime algorithms: The case of independent processes. Tech. rep., CS Department, Technion, Haifa, Israel.
11. Finkelstein L, Markovitch S, Rivlin E (2003) Optimal schedules for parallelizing anytime algorithms: The case of shared resources. Journal of Artificial Intelligence Research 19:73-138.
12. Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G (2008) Longitudinal Data Analysis. Chapman & Hall/CRC.
13. Fleming T, Harrington D (1991) Counting processes and survival analysis. Wiley, New York, NY.
14. Frost D, Rish I, Vila L (1997) Summarizing CSP hardness with continuous probability distributions. In: Kuipers B, et al. (eds) Fourteenth National Conference on Artificial Intelligence, pp 327-333.
15. Gagliolo M, Schmidhuber J (2006a) Impact of censored sampling on the performance of restart strategies. In: Benhamou F (ed) Principles and Practice of Constraint Programming, Springer, pp 167-181.
16. Gagliolo M, Schmidhuber J (2006b) Learning dynamic algorithm portfolios. Annals of Mathematics and Artificial Intelligence 47(3-4):295-328.
17. Gagliolo M, Schmidhuber J (2007) Learning restart strategies. In: Veloso MM (ed) Twentieth International Joint Conference on Artificial Intelligence, vol 1, AAAI, pp 792-797.
18. Gagliolo M, Schmidhuber J (2008a) Algorithm selection as a bandit problem with unbounded losses. Tech. Rep. IDSIA - 07 - 08 IDSIA.
19. Gagliolo M, Schmidhuber J (2008b) Towards distributed algorithm portfolios. In: Corchado JM, et al. (eds) International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2008), Springer, pp 634-643.
20. Gent I, Walsh T (1999) The search for satisfaction. Tech. rep., Dept. of Computer Science, University of Strathclyde.
21. Gomes CP, Selman B (2001) Algorithm portfolios. Artificial Intelligence 126(1- 2):43-62.
22. Gomes CP, Selman B, Kautz H (1998) Boosting combinatorial search through randomization. In: Mostow J, et al. (eds) Fifteenth National Conference on Artificial Intelligence, pp 431-437.
23. Gomes CP, Selman B, Crato N, Kautz H (2000) Heavy-tailed phenomena in satisfiability and constraint satisfaction problems. Journal of Automated Reasoning 24(1-2):67-100.
24. Hansen EA, Zilberstein S (2001) Monitoring and control of anytime algorithms: A dynamic programming approach. Artificial Intelligence 126(1-2):139-157.
25. Hogg T, Williams CP (1994) The hardest constraint problems: A double phase transition. Artificial Intelligence 69(1-2):359-377.
26. Hoos HH (1999) On the run-time behaviour of stochastic local search algorithms for SAT. In: Hendler J, et al. (eds) Sixteenth National Conference on Artificial Intelligence, pp 661-666.
27. Hoos HH (2002) A mixture-model for the behaviour of SLS algorithms for SAT. In: Hendler JA (ed) Eighteenth National Conference on Artificial Intelligence, pp 661-667.
28. Hoos HH, Stutzle T (2000) SATLIB: An online resource for research on SAT. In: Gent I, et al. (eds) SAT 2000 - Highlights of Satisfiability Research in the Year 2000, IOS, pp 283-292.
29. Hoos HH, Stutzle T (2004) Stochastic Local Search: Foundations & Applications. Morgan Kaufmann.
30. Huberman BA, Lukose RM, Hogg T (1997) An economic approach to hard computational problems. Science 27:51-53.
31. Ibrahim JG, Chen MH, Sinha D (2001) Bayesian Survival Analysis. Springer.
32. Kaplan EL, Meyer P (1958) Nonparametric estimation from incomplete samples. Journal of the American Statistical Association 73:457-481.
33. Keilegom IV, Akritas M, Veraverbeke N (2001) Estimation of the conditional distribution in regression with censored data: A comparative study. Computational Statistics and Data Analysis 35:487-500.
34. Klein JP, Moeschberger ML (2003) Survival Analysis: Techniques for Censored and Truncated Data, 2nd edn. Springer.
35. Leyton-Brown K, Nudelman E, Shoham Y (2002) Learning the empirical hardness of optimization problems: The case of combinatorial auctions. In: Van Hentenryck P (ed) Principles and Practice of Constraint Programming, pp 91-100.
36. Li CM, Anbulagan A. (1997) Heuristics based on unit propagation for satisfiability problems. In: Georgeff MP, et al. (eds) Fifteenth International Joint Conference on Artificial Intelligence, pp 366-371.
37. Li CM, Huang W (2005) Diversification and determinism in local search for satisfiability. In: Bacchus F, et al. (eds) Theory and Applications of Satisfiability Testing, Springer, pp 158-172.
38. Li G, Doss H (1995) An approach to nonparametric regression for life history data using local linear fitting. Annals of Statistics 23:787-823.
39. Liang K, Self S, Bandeen-Roche K, Zeger S (1995) Some recent developments for regression analysis of multivariate failure time data. Lifetime Data Analysis 1:403-415.
40. Luby M, Sinclair A, Zuckerman D (1993) Optimal speedup of las vegas algorithms. Information Processing Letters 47(4):173-180.
41. Machin D, Cheung Y, Parmar M (2006) Survival Analysis. A Practical Approach. Wiley, UK, second edition.
42. MacKay DC (2002) Information Theory, Inference and Learning Algorithms. Cambridge. University Press
43. Mitchell D, Selman B, Levesque H (1992) Hard and easy distributions of SAT problems. In: Swartout W (ed) Tenth National Conference on Artificial Intelligence, pp 459-465.
44. Nelson W (1972) Theory and applications of hazard plotting for censored failure data. Technometrics 14:945-965.
45. Nelson W (1982) Applied Life Data Analysis. Wiley, New York, NY.
46. Nielsen J, Linton O (1995) Kernel estimation in a nonparametric marker dependent hazard model. Annals of Statistics 23:1735-1748.
47. Nudelman E, Brown KL, Hoos HH, Devkar A, Shoham Y (2004) Understanding random SAT: Beyond the clauses-to-variables ratio. In:WallaceM(ed) Principles and Practice of Constraint Programming, Springer, pp 438-452.
48. Pintilie M (2006) Competing Risks A practical perspective. Wiley, New York, NY.
49. Putter H, Fiocco M, Geskus R (2006) Tutorial in biostatistics: Competing risks and multi-state models. Statistics in Medicine 26:2389-2430.
50. Rice JR (1976) The algorithm selection problem. In: Rubinoff M, Yovits MC (eds) Advances in computers, vol 15, Academic, New York, pp 65-118.
51. Spierdijk L (2005) Nonparametric conditional hazard rate estimation: A local linear approach. Tech. Rep. TW Memorandum, University of Twente.
52. Tsang E (1993) Foundations of Constraint Satisfaction. Academic, London and San Diego.
53. Tsiatis A (1975) A nonidentifiability aspect of the problem of competing risks. Proceedings of the National Academy of Sciences of the USA 72(1):20-22.
54. Vilalta R, Drissi Y (2002) A perspective view and survey of meta-learning. Artificial Intelligence Review 18(2):77-95.
55. Wang JL (2005) Smoothing hazard rate. In: Armitage P, et al. (eds) Encyclopedia of Biostatistics, 2nd Edition, vol 7, Wiley, pp 4986-4997.
56. Wichert L, Wilke RA (2005) Application of a simple nonparametric conditional quantile function estimator in unemployment duration analysis. Tech. Rep. ZEW Discussion Paper No. 05-67, Centre for European Economic Research.
57. Xu L, Hutter F, Hoos HH, Leyton-Brown K (2008) SATzilla: Portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32 (2008) 565-606.