52 citations · 86 across the 9 of their papers we have counts for
9 papers
Rare Gems: Finding Lottery Tickets at Initialization
Kartik Sreenivasan, Jy-yong Sohn, Liu Yang +6
Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming "train, prune, re-train" approach. Frank…
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…
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…
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…
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…
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…
Surrogate Losses in Passive and Active Learning
Steve Hanneke, Liu Yang
Active learning is a type of sequential design for supervised machine learning, in which the learning algorithm sequentially requests the labels of selected instances from a large…
Bayesian Active Distance Metric Learning
Liu Yang, Rong Jin, Rahul Sukthankar
Distance metric learning is an important component for many tasks, such as statistical classification and content-based image retrieval. Existing approaches for learning distance m…
Active Property Testing
Maria-Florina Balcan, Eric Blais, Avrim Blum +1
One of the motivations for property testing of boolean functions is the idea that testing can serve as a preprocessing step before learning. However, in most machine learning appli…