Ranking Methods in Machine Learning
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1 Ranking Methods in Machine Learning A Tutorial Introduction Shivani Agarwal Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology
2 Example 1: Recommendation Systems
3 Example 2: Information Retrieval
4 Example 2: Information Retrieval information
5 Example 2: Information Retrieval information
6 Example 3: Drug Discovery Problem: Millions of structures in a chemical library. How do we identify the most promising ones?
7 Example 4: Bioinformatics Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, nonmendelian diseases... Nature 405: , 2000
8 Example 4: Bioinformatics With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of nonmendelian disorders based on large-scale genomewide studies... Nature 405: , 2000
9 Types of Ranking Problems Instance Ranking Label Ranking Subset Ranking Rank Aggregation?
10 Instance Ranking doc1 > doc2, 10 doc3 > doc5, 20
11 Label Ranking doc1 doc2 sports > politics health > money science > sports money > politics
12 Subset Ranking > query 1 doc1 > doc2, doc3 doc5 query 2 doc2 > doc4, doc11 > doc3
13 Rank Aggregation query 1 results of search engine 1 results of search engine 2 desired ranking query 2 results of search engine 1 results of search engine 2 desired ranking
14 Types of Ranking Problems Instance Ranking Label Ranking Subset Ranking Rank Aggregation? This tutorial
15 Tutorial Road Map Part I: Theory & Algorithms Bipartite Ranking k-partite Ranking Ranking with Real-Valued Labels General Instance Ranking RankSVM RankBoost RankNet Part II: Applications Applications to Bioinformatics Applications to Drug Discovery Subset Ranking and Applications to Information Retrieval Further Reading & Resources
16 Part I Theory & Algorithms [for Instance Ranking]
17 Bipartite Ranking Relevant (+) doc1+ doc2+ doc3+ Irrelevant (-) doc1- doc2- doc3- doc4-
18 Bipartite Ranking
19 Bipartite Ranking
20 Is Bipartite Ranking Different from Binary Classification?
21 Is Bipartite Ranking Different from Binary Classification?
22 Is Bipartite Ranking Different from Binary Classification?
23 Is Bipartite Ranking Different from Binary Classification?
24 Is Bipartite Ranking Different from Binary Classification?
25 Is Bipartite Ranking Different from Binary Classification?
26 Bipartite Ranking: Basic Algorithmic Framework
27 Bipartite RankSVM Algorithm [Herbrich et al, 2000; Joachims, 2002; Rakotomamonjy, 2004]
28 Bipartite RankSVM Algorithm
29 Bipartite RankBoost Algorithm [Freund et al, 2003]
30 Bipartite RankBoost Algorithm
31 Bipartite RankNet Algorithm [Burges et al, 2005]
32 k-partite Ranking Rating k doc1 k doc2 k doc3 k Rating 2 doc1 2 doc2 2 doc3 2 doc4 2 Rating 1 doc1 1 doc2 1 doc3 1 doc4 1
33 k-partite Ranking
34 k-partite Ranking: Basic Algorithmic Framework
35 Ranking with Real-Valued Labels doc1 y1 doc2 y2 doc3 y3
36 Ranking with Real-Valued Labels
37 Ranking with Real-Valued Labels: Basic Algorithmic Framework
38 General Instance Ranking doc1 > doc1, r1 doc2 > doc2, r2
39 General Instance Ranking r i
40 General Instance Ranking
41 General Instance Ranking: Basic Algorithmic Framework
42 General RankSVM Algorithm [Herbrich et al, 2000; Joachims, 2002]
43 General RankBoost Algorithm [Freund et al, 2003]
44 General RankNet Algorithm [Burges et al, 2005]
45 Tutorial Road Map Part I: Theory & Algorithms Bipartite Ranking k-partite Ranking Ranking with Real-Valued Labels General Instance Ranking RankSVM RankBoost RankNet Part II: Applications Applications to Bioinformatics Applications to Drug Discovery Subset Ranking and Applications to Information Retrieval Further Reading & Resources
46 Part II Applications [and Subset Ranking]
47 Application to Bioinformatics Human genetics is now at a critical juncture. The molecular methods used successfully to identify the genes underlying rare mendelian syndromes are failing to find the numerous genes causing more common, familial, nonmendelian diseases... Nature 405: , 2000
48 Application to Bioinformatics With the human genome sequence nearing completion, new opportunities are being presented for unravelling the complex genetic basis of nonmendelian disorders based on large-scale genomewide studies... Nature 405: , 2000
49 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
50 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
51 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
52 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
53 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
54 Identifying Genes Relevant to a Disease Using Microarrray Gene Expression Data
55 Formulation as a Bipartite Ranking Problem Relevant Not relevant
56 Microarray Gene Expression Data Sets [Golub et al, 1999; Alon et al, 1999]
57 Selection of Training Genes
58 Top-Ranking Genes for Leukemia Returned by RankBoost [Agarwal & Sengupta, 2009]
59 Biological Validation [Agarwal et al, 2010]
60 Application to Drug Discovery Problem: Millions of structures in a chemical library. How do we identify the most promising ones?
61 Formulation as a Ranking Problem with Real-Valued Labels
62 Cheminformatics Data Sets [Sutherland et al, 2004]
63 DHFR Results Using RankSVM [Agarwal et al, 2010]
64 Application to Information Retrieval (IR)
65 Learning to Rank in IR
66 Learning to Rank in IR
67 Learning to Rank in IR
68 Learning to Rank in IR
69 Learning to Rank in IR
70 Learning to Rank in IR
71 Learning to Rank in IR
72 Learning to Rank in IR
73 Learning to Rank in IR
74 Learning to Rank in IR
75 General Subset Ranking > query 1 doc1 > doc2, doc3 doc5 query 2 doc2 > doc4, doc11 > doc3
76 General Subset Ranking
77 Subset Ranking with Real-Valued Relevance Labels query 1 doc1 y1, doc2 y2, doc3 y3 query 2 doc1 y1, y2, y3 doc2 doc3
78 Subset Ranking with Real-Valued Relevance Labels
79 RankSVM Applied to IR/Subset Ranking Standard RankSVM [Joachims, 2002]
80 RankSVM Applied to IR/Subset Ranking RankSVM with Query Normalization & Relevance Weighting [Agarwal & Collins, 2010; also Cao et al, 2006]
81 Ranking Performance Measures in IR Mean Average Precision (MAP)
82 Ranking Performance Measures in IR Normalized Discounted Cumulative Gain (NDCG)
83 Ranking Algorithms for Optimizing MAP/NDCG
84 LETOR 3.0/OHSUMED Data Set [Liu et al, 2007]
85 OHSUMED Results NDCG
86 OHSUMED Results MAP
87 Further Reading & Resources [Incomplete!]
88 Early Papers on Ranking W. W. Cohen, R. E. Schapire, and Y. Singer, Learning to order things, Journal of Artificial Intelligence Research, 10: , R. Herbrich, T. Graepel, and K. Obermayer, Large margin rank boundaries for ordinal regression. Advances in Large Margin Classifiers, T. Joachims, Optimizing search engines using clickthrough data, KDD Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4: , C.J.C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, G. Hullender, Learning to rank using gradient descent, ICML 2005.
89 Generalization Bounds for Ranking S. Agarwal, T. Graepel, R. Herbrich, S. Har-Peled, D. Roth, Generalization bounds for the area under the ROC curve, Journal of Machine Learning Research, 6: , S. Agarwal, P. Niyogi, Generalization bounds for ranking algorithms via algorithmic stability, Journal of Machine Learning Research, 10: , C. Rudin, R. Schapire, Margin-based ranking and an equivalence between AdaBoost and RankBoost, Journal of Machine Learning Research, 10: , 2009
90 Bioinformatics/Drug Discovery Applications S. Agarwal and S. Sengupta, Ranking genes by relevance to a disease, CSB S. Agarwal, D. Dugar, and S. Sengupta, Ranking chemical structures for drug discovery: A new machine learning approach. Journal of Chemical Information and Modeling, DOI /ci , 2010.
91 Other Applications Natural Language Processing M. Collins and T. Koo, Discriminative reranking for natural language parsing, Computational Linguistics, 31:25 69, Collaborative Filtering M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola, CofiRank - Maximum margin matrix factorization for collaborative ranking, NIPS Manhole Event Prediction C. Rudin, R. Passonneau, A. Radeva, H. Dutta, S. Ierome, and D. Isaac, A process for predicting manhole events in Manhattan, Machine Learning, DOI /s , 2010.
92 IR Ranking Algorithms Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Hunag, and H.W. Hon, Adapting ranking SVM to document retrieval, SIGIR C.J.C. Burges, R. Ragno, and Q.V. Le, Learning to rank with non-smooth cost functions. NIPS J. Xu and H. Li, AdaRank: A boosting algorithm for information retrieval. SIGIR Y. Yue, T. Finley, F. Radlinski, and T. Joachims, A support vector method for optimizing average precision. SIGIR M. Taylor, J. Guiver, S. Robertson, T. Minka, Softrank: optimizing non-smooth rank metrics. WSDM 2008.
93 IR Ranking Algorithms S. Chakrabarti, R. Khanna, U. Sawant, and C. Bhattacharyya, Structured learning for nonsmooth ranking losses. KDD D. Cossock and T. Zhang, Statistical analysis of Bayes optimal subset ranking, IEEE Transactions on Information Theory, 54: , T. Qin, X.D. Zhang, M.F. Tsai, D.S. Wang, T.Y. Liu, and H. Li. Query-level loss functions for information retrieval. Information Processing and Management, 44: , O. Chapelle and M. Wu, Gradient descent optimization of smoothed information retrieval metrics. Information Retrieval (To appear), S. Agarwal and M. Collins, Maximum margin ranking algorithms for information retrieval, ECIR 2010.
94 NIPS Workshop 2005 Learning to Rank SIGIR Workshops Learning to Rank for Information Retrieval NIPS Workshop 2009 Advances in Ranking American Institute of Mathematics Workshop in Summer 2010 The Mathematics of Ranking
95 Tutorial Articles & Books Tie-Yan Liu, Learning to Rank for Information Retrieval, Foundations & Trends in Information Retrieval, Shivani Agarwal, A Tutorial Introduction to Ranking Methods in Machine Learning, In preparation. Shivani Agarwal (Ed.), Advances in Ranking Methods in Machine Learning, Springer-Verlag, In preparation.
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