Machine Learning | Yahoo! Research

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.

Recent Publications

Fast Solvers and Efficient Implementations for Distance Metric Learning Kilian Q. Weinberger; Lawrence K. Saul, International Conference on Machine Learning (ICML), 2008 [view abstract]
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]
Enhanced Hierarchical Classification via Isotonic Smoothing Kunal Punera; Joydeep Ghosh, 17th International World Wide Web Conference (WWW), 2008 [view abstract]
Approximation Algorithms for Co-Clustering Aris Anagnostopoulos; Anirban Dasgupta; Ravi Kumar, PODS, 2008 [view abstract]
Regret Minimization in Games with Incomplete Information Zinkevich, M. ; Johanson, M. ; Bowling, M. ; Piccione, C., Neural Information Processing Systems, 2008
Computing Robust Counter-Strategies Johanson, M. ; Zinkevich, M. ; Bowling, M., Neural Information Processing Systems, 2008
Metric Learning with Convex Optimization K. Q. Weinberger, Computer and Information Science, University of Pennsylvania, 2007 [view abstract]
Soft Cluster Ensembles Kunal Punera; Joydeep Ghosh; J Oliveira and W Pedrycz (Eds), Advances in Fuzzy Clustering and Its Applications, Wiley, 2007 [view abstract]
Estimating Rates of Rare Events at Multiple Resolutions D. Agarwal; A. Broder; D. Chakrabarti; D. Diklic; V. Josifovski; M. Sayyadian, KDD, 2007
Feature Selection Methods for Text Classification Anirban Dasgupta;Petros Drineas;Boulos Harb;Vanja Josifovski;Michael Mahoney, KDD, 2007
Parsimonious Explanations of Change in Hierarchical Data Barman, Dhiman ; Korn, Flip ; Srivastava, Divesh ; Gunopulos, Dimitrios ; Young, Neal E. ; Agarwal, Deepak, ICDE, 2007
Predictive discrete latent factor models for large scale dyadic data Agarwal, Deepak ; Merugu, Srujana, KDD, 2007
Efficient and effective explanation of change in hierarchical summaries Agarwal, Deepak ; Barman, Dhiman ; Gunopulos, Dimitrios ; Young, Neal E. ; Korn, Flip ; Srivastava, Divesh, KDD, 2007
Bandits for Taxonomies: A Model-based Approach Pandey, Sandeep ; Agarwal, Deepak ; Chakrabarti, Deepayan ; Josifovski, Vanja, SDM, 2007
Detecting anomalies in cross-classified streams: a Bayesian approach Agarwal, Deepak, Knowl. Inf. Syst., 2007
Exploring Massive Learning via a Prediction System Omid Madani, AAAI Fall Symposium Series, 2007 [view abstract]
A Hierarchy of Prescriptive Goals for Multiagent Learning Zinkevich, M. ; Greenwald, A. ; Littman, M., Artificial Intelligence, 2007
A New Algorithm for Generating Equilibria in Massive Zero-Sum Games Zinkevich, M. ; Bowling, M. ; Burch, N., Twenty-Second Conference on Artificial Intelligence (AAAI-07), 2007
Subgradient Methods for Structured Prediction Ratliff, N. ; Bagnell, D. ; Zinkevich, M., Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007
Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions Christian Walder , Bernhard Scholkopf, Olivier Chapelle, Advances in Neural Information Processing Systems 19, MIT Press, 2007

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Recent Publications