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The Machine Learning group is a team of experts in computer science, statistics, mathematical optimization, and automatic control. We focus on making computers learn abstractions, patterns, conditional probability distributions, and policies from web scale data with the goal to improve the online experience for Yahoo users, partner publishers, and advertisers.
Featured Project
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Sparta
State-of-the-art spam detection that has dramatically reduced the amount of spam mail that can leak through to the in-boxes of Yahoo! Mail users.
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Recent Publications
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Inferring the structure and scale of modular networks
Jake M. Hofman; Chris H. Wiggins, 6th International Conference on Mining and Learning with Graphs, 2008
[view abstract]
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Chat mining: predicting user and message attributes in computer-mediated communication
T. Kucukyilmaz; B.B. Cambazoglu; F. Can; C. Aykanat, Information Processing & Management, Elsevier, 2008, 4
[view abstract]
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A Bayesian approach to network modularity
Jake M. Hofman; Chris H. Wiggins, Physical Review Letters, 2008
[view abstract]
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Mapping uncharted waters: exploratory analysis, visualization, and clustering of oceanographic data.
J. M. Lewis, P. M. Hull, K. Q. Weinberger, and L. K. Saul, International Conference on Machine Learning Applications, 2008
[view abstract]
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Fast Solvers and Efficient Implementations for Distance Metric Learning
Kilian Q. Weinberger; Lawrence K. Saul, International Conference on Machine Learning (ICML), 2008
[view abstract]
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Statistical Challenges in Online Advertising
Deepak Agarwal; Deepayan Chakrabarti, CIKM 2008, 2008
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Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
Deepak Agarwal; Liang Zhang, NIPS, 2008
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Online Models for Content Optimization
Deepak Agarwal, Bee-Chung Chen, Pradheep Elango, Raghu Ramakrishnan, Nitin Motgi, Scott Roy, Joe Zachariah, NIPS, 2008
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Approximation Algorithms for Co-Clustering
Aris Anagnostopoulos; Anirban Dasgupta; Ravi Kumar, PODS, 2008
[view abstract]
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Large Margin Taxonomy Embedding with an Application to Document Categorization
Kilian Weinberger; Olivier Chapelle, Neural Information Processing Systems (NIPS), 2008
[view abstract]
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On the Hardness of Finding Symmetries
Shravan M Narayamurthy;Balaraman Ravindran, International Conference on Machine Learning (ICML), 2008
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Robust Reductions from Ranking to Classification
Maria-Florina Balcan; Nikhil Bansal; Alina Beygelzimer; Don Coppersmith; John Langford; Gregory B. Sorkin, Machine Learning Journal, Springer, 2008, 1-2
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Exploration Scavenging
John Langford; Alex Strehl; Jennifer Wortman, ICML, 2008
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Webspam Identification Through Content and Hyperlinks
Jacob Abernethy; Olivier Chapelle; Carlos Castillo, Fourth International Workshop on Adversarial Information Retrieval on the Web, ACM Press, 2008
[view abstract]
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Enhanced Hierarchical Classification via Isotonic Smoothing
Kunal Punera; Joydeep Ghosh, 17th International World Wide Web Conference (WWW), 2008
[view abstract]
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Regret Minimization in Games with Incomplete Information
Zinkevich, M. ; Johanson, M. ; Bowling, M. ; Piccione, C., Neural Information Processing Systems, 2008
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Computing Robust Counter-Strategies
Johanson, M. ; Zinkevich, M. ; Bowling, M., Neural Information Processing Systems, 2008
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Bandits for Taxonomies: A Model-based Approach
Pandey, S. ; Agarwal, D. ; Chakrabarti, D. ; Josifovski, V., SDM, 2007
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Feature Selection Methods for Text Classification
Anirban Dasgupta;Petros Drineas;Boulos Harb;Vanja Josifovski;Michael Mahoney, KDD, 2007
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Online Linear Regression and Its Application to Model-Based Reinforcement Learning
Alexander L. Strehl; Michael L. Littman, NIPS, 2007
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