Significant changes in the instance distribution or associated cost function of a learning problem require one to reoptimize a previously-learned classifier to work under new cond...
Chris Bourke, Kun Deng, Stephen D. Scott, Robert E...
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
We revise the Volume Algorithm (VA) for linear programming and relate it to bundle methods. When first introduced, VA was presented as a subgradient-like method for solving the ori...
Laura Bahiense, Nelson Maculan, Claudia A. Sagasti...
We present a polynomial-time algorithm that provably recovers the signer's secret DSA key when a few bits of the random nonces k (used at each signature generation) are known ...
In this paper, we design and implement an efficient technique for parallel evidence propagation on state-of-the-art multicore processor systems. Evidence propagation is a major ste...