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VL
2010
IEEE
216views Visual Languages» more  VL 2010»
14 years 8 months ago
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs
Many machine-learning algorithms learn rules of behavior from individual end users, such as taskoriented desktop organizers and handwriting recognizers. These rules form a “prog...
Todd Kulesza, Simone Stumpf, Margaret M. Burnett, ...
CDC
2010
IEEE
106views Control Systems» more  CDC 2010»
14 years 5 months ago
Observational learning in an uncertain world
We study a model of observational learning in social networks in the presence of uncertainty about agents' type distributions. Each individual receives a private noisy signal ...
Daron Acemoglu, Munther A. Dahleh, Asuman E. Ozdag...
CORR
2011
Springer
183views Education» more  CORR 2011»
14 years 4 months ago
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
— We address the sparse signal recovery problem in the context of multiple measurement vectors (MMV) when elements in each nonzero row of the solution matrix are temporally corre...
Zhilin Zhang, Bhaskar D. Rao
AAAI
2011
13 years 10 months ago
User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models
With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy conc...
Justin Cranshaw, Jonathan Mugan, Norman M. Sadeh
KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 1 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing