activity
20112017
most citedActivized Learning: Transforming Passive to Active with Improved Label Complexity

37 citations · 65 across the 8 of their papers we have counts for

collaborators

8 papers

cs.DS2017

Testing Piecewise Functions

Steve Hanneke, Liu Yang

This work explores the query complexity of property testing for general piecewise functions on the real line, in the active and passive property testing settings. The results are p…

cs.LG2017★ 1 cited

Learning with Changing Features

Amit Dhurandhar, Steve Hanneke, Liu Yang

In this paper we study the setting where features are added or change interpretation over time, which has applications in multiple domains such as retail, manufacturing, finance. I…

cs.LG2015★ 2 cited

Statistical Learning under Nonstationary Mixing Processes

Steve Hanneke, Liu Yang

We study a special case of the problem of statistical learning without the i.i.d. assumption. Specifically, we suppose a learning method is presented with a sequence of data points…

cs.LG2015★ 1 cited

Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks

Liu Yang, Steve Hanneke, Jaime Carbonell

We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target fu…

cs.LG2015★ 4 cited

Learning with a Drifting Target Concept

Steve Hanneke, Varun Kanade, Liu Yang

We study the problem of learning in the presence of a drifting target concept. Specifically, we provide bounds on the error rate at a given time, given a learner with access to a h…

cs.LG2014★ 2 cited

Minimax Analysis of Active Learning

Steve Hanneke, Liu Yang

This work establishes distribution-free upper and lower bounds on the minimax label complexity of active learning with general hypothesis classes, under various noise models. The r…