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- Steve Hanneke
- ICML
- 2007

We study the label complexity of pool-based active learning in the agnostic PAC model. Specifically, we derive general bounds on the number of label requests made by the A2 algorithm proposed byâ€¦ (More)

- Steve Hanneke, Eric P. Xing
- SNA@ICML
- 2006

We propose a family of statistical models for social network evolution over time, which represents an extension of Exponential Random Graph Models (ERGMs). Many of the methods for ERGMs are readilyâ€¦ (More)

- Steve Hanneke
- COLT 2009
- 2009

We study the rates of convergence in classification error achievable by active learning in the presence of label noise. Additionally, we study the more general problem of active learning with aâ€¦ (More)

- Fan Guo, Steve Hanneke, Wenjie Fu, Eric P. Xing
- ICML
- 2007

A plausible representation of relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network which is topologically rewiring andâ€¦ (More)

- Maria-Florina Balcan, Steve Hanneke, Jennifer Wortman Vaughan
- Machine Learning
- 2008

We describe and explore a new perspective on the sample complexity of active learning. In many situations where it was generally believed that active learning does not help, we show that activeâ€¦ (More)

- Steve Hanneke
- COLT
- 2007

We study the label complexity of pool-based active learning in the PAC model with noise. Taking inspiration from extant literature on Exact learning with membership queries, we derive upper and lowerâ€¦ (More)

- Steve Hanneke
- 2009

I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of convergence achievable by active learning, underâ€¦ (More)

- Steve Hanneke
- Journal of Machine Learning Research
- 2012

We study the theoretical advantages of active learning over passive learning. Specifically, we prove that, in noise-free classifier learning for VC classes, any p assive learning algorithm can beâ€¦ (More)

- Steve Hanneke
- 2013

Active learning is a protocol for supervised machine learning, in which a learning algorithm sequentially requests the labels of selected data points from a large pool of unlabeled data. Thisâ€¦ (More)

- Steve Hanneke
- ICML
- 2006

I consider the setting of transductive learning of vertex labels in graphs, in which a graph with n vertices is sampled according to some unknown distribution; there is a true labeling of theâ€¦ (More)