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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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Explore/Exploit Schemes for Web Content Optimization (best paper award)
Deepak Agarwal; Bee-Chung Chen; Pradheep Elango, IEEE International Conference on Data Mining, 2009
[view abstract]
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Regression based Latent Factor Models
Deepak Agarwal; Bee-Chung Chen, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2009
[view abstract]
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Spatio-Temporal Models for Estimating Click-through Rate
Deepak Agarwal; Bee-Chung Chen; Pradheep Elango, The 18th International World Wide Web Conference, 2009
[view abstract]
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Efficient and Accurate Lp-norm Multiple Kernel Learning
M. Kloft; U. Brefeld; S. Sonnenburg; P. Laskov; K.-R. Müller; A. Zien, NIPS, 2009
[view abstract]
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Wikipedia-based Semantic Interpretation for Natural Language Processing
Evgeniy Gabrilovich; Shaul Markovitch, Journal of Artificial Intelligence Research (JAIR), 2009
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Matrix Factorization Techniques for Recommender Systems
Yehuda Koren; Robert Bell; Chris Volinsky, IEEE Computer, 2009, 8
[view abstract]
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The Million Dollar Programming Prize
Robert Bell; Jim Bennett; Yehuda Koren; Chris Volinsky, IEEE Spectrum, IEEE, 2009
[view abstract]
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Collaborative Filtering with Temporal Dynamics
Yehuda Koren, KDD 2009, ACM, 2009
[view abstract]
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Predictive Indexing for Fast Search
Sharad Goel; John Langford; Alex Strehl, Neural Information Processing Systems (NIPS), 2008
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Structured learning for non-smooth ranking losses
Soumen Chakrabarti; Rajiv Khanna; Uma Sawant; Chiru Bhattacharyya, KDD, 2008
[view abstract]
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Approximation Algorithms for Co-Clustering
Aris Anagnostopoulos; Anirban Dasgupta; Ravi Kumar, PODS, 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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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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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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