Fast learning of k-term DNF formulas with queries
A PAC-Style Model for Learning from Labeled and Unlabeled Data
Kernels as features: On kernels, margins, and low-dimensional mappings
The Johnson-Lindenstrauss Transform Itself Preserves Differential Privacy
Combining labeled and unlabeled data with co-training
Practical privacy
2012 IEEE 53rd Annual Symposium on Foundations of Computer Science
Proceedings of the twenty-seventh annual ACM symposium on Theory of computing - STOC ’95
Machine Learning
Lecture Notes in Computer Science
Proceedings of the twenty-fourth annual ACM symposium on Theory of computing - STOC ’92
Proceedings of the eleventh annual conference on Computational learning theory - COLT’ 98
Santosh Vempala
Maria-Florina Balcan
Or Sheffet
Anupam Datta
Jeremiah Blocki
Prasad Chalasani
Discovering health-related knowledge in social media using ensembles of heterogeneous features
Toward a Unified Theory of Sparse Dimensionality Reduction in Euclidean Space
Beyond worst-case analysis in private singular vector computation
Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems
A technique for upper bounding the spectral norm with applications to learning