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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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Active Learning from Multiple Knowledge Sources
Yan Yan; Romer Rosales; Glenn Fung; Faisal Farooq; Bharat Rao; Jennifer Dy, International Conference on Artificial Intelligence and Statistics, 2012
[view abstract]
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Post-Click Conversion Modeling and Analysis for NGD Display Advertising
Romer Rosales; Haibin Cheng; Eren Manavoglu, International Conference on Web Search and Data Mining, 2012
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Build Your Own Music Recommender by Modeling Internet Radio Streams
Natalie Aizenberg; Yehuda Koren; Oren Somekh, WWW'2012, ACM, 2012
[view abstract]
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Mining Web Query Logs to Analyze Political Issues
Ingmar Weber; Venkata Rama Kiran Garimella; Erik Borra, ACM Web Science Conference, 2012
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Scalable Inference in Latent Variable Models
Amr Ahmed;Mohamed Aly;Joseph Gonzalez;Shravan Narayanamurthy;Alexander J. Smola, Proceedings of the Fifth International Conference on Web Search and Web Data Mining, WSDM, 2012
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Web-Scale User Modeling for Targeting
Mohamed Aly;Andrew Hatch;Vanja Josifovski;Vijay K. Narayanan, Proceedings of the 21s International World Wide Web Conference, 2012
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Care to Comment? Recommendations for Commenting on News Stories
Erez Shmueli; Amit Kagian; Yehuda Koren; Ronny Lempel, WWW'12, ACM, 2012
[view abstract]
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A large-scale sentiment analysis for Yahoo! Answers
O. Kucuktunc; B. B. Cambazoglu; I. Weber; H. Ferhatosmanoglu, Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 2012
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Answers, not Links -- Extracting Tips from Yahoo! Answers to Address How-To Web Queries
Ingmar Weber; Antti Ukkonen; Aris Gionis, WSDM, 2012
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Active Learning from Crowds
Yan Yan; Romer Rosales; Glenn Fung; Jennifer Dy, International Conference on Machine Learning, 2011
[view abstract]
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Bayesian Co-training
Shipeng Yu; Balaji Krishnapuram; Romer Rosales; Bharat Rao, Journal of Machine Learning Research, MIT, 2011
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Yahoo! Music Recommendations: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy
Gideon Dror; Noam Koenigstein; Yehuda Koren, ACM Recommender Systems 2011 (RecSys'11), ACM, 2011
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Results of the Active Learning Challenge
I Guyon; G. Cawley; G. Dror; V. Lemaire, Journal of Machine Learning Research, W&CP, 2011
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Design and Analysis of the Unsupervised and Transfer Learning Challenge
I. Guyon; D. Aha ;G. Dror;V. Lemaire; G. Taylor., IJCNN - International Joint Conference on Neural Networks, 2011, 2011
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Yahoo! Music Recommendations: Modeling Music Ratings with Temporal Dynamics and Item Taxonomy
G. Dror; N. Koenigstein; Y. Koren, Recsys 2011, 2011
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Machine learned job recommendation
I. K. Paparrizos; B. B. Cambazoglu; A. Gionis:, Proceedings of the 5th ACM International Conference on Recommender Systems, 2011
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OrdRec: An Ordinal Model for Predicting Personalized Item Rating Distributions
Yehuda Koren; Joe Sill, ACM Recommender Systems 2011 (RecSys'11), ACM, 2011
[view abstract]
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An empirical evaluation of Thompson sampling
Olivier Chapelle; Lihong Li, NIPS, 2011
[view abstract]
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Contextual bandits with linear payoff functions
Wei Chu; Lihong Li; Lev Reyzin; Robert E. Schapire, AISTATS, 2011
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Contextual Bandit Algorithms with Supervised Learning Guarantees
Alina Beygelzimer; John Langford; Lihong Li; Lev Reyzin; Robert E. Schapire, AISTATS, 2011
[view abstract]
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