We propose a formulation of the Decision Tree learning algorithm in the Compression settings and derive tight generalization error bounds. In particular, we propose Sample Compres...
This paper develops easily computed, tight bounds on Generalized Linear Predictors and instrumental variable estimators when outcome data are partially identi…ed. A salient exam...
Recent studies have suggested that the soft-error rate in microprocessor logic will become a reliability concern by 2010. This paper proposes an efficient error detection techniqu...
Jared C. Smolens, Brian T. Gold, Jangwoo Kim, Baba...
Assuming iterative decoding for binary erasure channels (BECs), a novel tree-based technique for upper bounding the bit error rates (BERs) of arbitrary, finite low-density parity-c...
Chih-Chun Wang, Sanjeev R. Kulkarni, H. Vincent Po...
We establish the first nontrivial lower bounds on timespace tradeoffs for the selection problem. We prove that any comparison-based randomized algorithm for finding the median ...