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- Alina Beygelzimer, Sham M. Kakade, John Langford
- ICML
- 2006

We present a tree data structure for fast nearest neighbor operations in general <i>n</i>-point metric spaces (where the data set consists of <i>n</i> points). The data structure requiresâ€¦ (More)

We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the assumption that theâ€¦ (More)

- Alina Beygelzimer, Sanjoy Dasgupta, John Langford
- ICML
- 2009

We present a practical and statistically consistent scheme for actively learning binary classifiers under general loss functions. Our algorithm uses importance weighting to correct sampling bias, andâ€¦ (More)

- Irina Rish, Mark Brodie, +4 authors Karina Hernandez
- IEEE Transactions on Neural Networks
- 2005

Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, weâ€¦ (More)

- Alina Beygelzimer, Daniel J. Hsu, John Langford, Tong Zhang
- NIPS
- 2010

We present and analyze an agnostic active learning algorith m that works without keeping a version space. This is unlike all previous approac hes where a restricted set of candidate hypotheses isâ€¦ (More)

We present a new algorithm, filter tree, for reducing (cost-sensitive) k-class classification to binary classification. The filter tree is provably consistent, in the sense that given an optimalâ€¦ (More)

- Alina Beygelzimer, John Langford
- KDD
- 2009

We present an algorithm, called the Offset Tree, for learning to make decisions in situations where the payoff of only one choice is observed, rather than all choices. The algorithm reduces thisâ€¦ (More)

- Maria-Florina Balcan, Nikhil Bansal, Alina Beygelzimer, Don Coppersmith, John Langford, Gregory B. Sorkin
- Machine Learning
- 2007

We reduce ranking, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC), to binary classification. The core theorem shows that aÂ binary classification regret of r on theâ€¦ (More)

We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels. We analyze a natural reduction of this problem to a set ofâ€¦ (More)

- Alice X. Zheng, Irina Rish, Alina Beygelzimer
- UAI
- 2005

We address the problem of active diagnosis on a Bayesian network using most-informative test selection. Finding an optimal subset of tests in this setting is intractable in general. We show that itâ€¦ (More)