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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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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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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
[view abstract]
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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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Linear-time estimators for propensity scores
Deepak Agarwal; Lihong Li; Alexander J. Smola, AISTATS, 2011
[view abstract]
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Scalable Distributed Inference of Dynamic User Interests for Behavioral Targeting
Amr Ahmed;Yucheng Low;Mohamed Aly;Alex Smola;Vanja Josifovski, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011
[view abstract]
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Unbiased online active learning in data streams
Wei Chu; Martin Zinkevich; Lihong Li; Achint Thomas; Belle Tseng, KDD, 2011
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Doubly robust policy evaluation and learning
Miroslav Dudik; John Langford; Lihong Li, ICML, 2011
[view abstract]
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Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms
Lihong Li; Wei Chu; John Langford; Xuanhui Wang, WSDM, 2011
[view abstract]
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Event Summarization using Tweets
Deepayan Chakrabarti;Kunal Punera, 5th International Conference on Weblogs and Social Media, ICWSM, 2011
[view abstract]
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Enhanced email spam filtering through combining similarity graphs
Anirban Dasgupta;Maxim Gurevich;Kunal Punera, 4th International Conference on Web Search and Web Data Mining, WSDM, 2011
[view abstract]
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Automatically Tagging Email by Leveraging Other Users' Folders
Yehuda Koren; Edo Liberty; Yoelle Maarek; Roman Sandler, KDD 2011: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, ACM, 2011
[view abstract]
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lp-Norm Multiple Kernel Learning
Marius Kloft; Ulf Brefeld; Soeren Sonnenburg; Alexander Zien, Journal of Machine Learning Research, 2011
[view abstract]
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