Several algorithms have been developed in the past that attempt to resolve categorial ambiguities in natural language text without recourse to syntactic or semantic level information. An innovative method (called "CLAWS") was recently developed by those working with the Lancaster -Oslo/Bergen Corpus of British English. This algorithm uses a systematic calculation based upon the probabilities of...

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission....

In statistical machine translation (SMT), the optimization of the system parameters to maximize translation accuracy is now a fundamental part of virtually all modern systems. In this article, we survey 12 years of research on optimization for SMT, from the seminal work on discriminative models (Och and Ney 2002) and minimum error rate training (Och 2003), to the most recent advances. Starting ...

Stochastic convex optimization, where the objective is the expectation of a random convex function, is an important and widely used method with numerous applications in machine learning, statistics, operations research and other areas. We study the complexity of stochastic convex optimization given only statistical query (SQ) access to the objective function. We show that well-known and popular...

Non-Negative Matrix Factorization (NMF) is a dimensionality reduction method that has been shown to be very useful for a variety of tasks in machine learning and data mining. One of the fastest algorithms for NMF is the Block Principal Pivoting method (BPP) of [6], which follows a block coordinate descent approach. The optimization in each iteration involves solving a large number of expensive ...

We analyze two communication-efficient algorithms for distributed optimization in statistical settings involving large-scale data sets. The first algorithm is a standard averaging method that distributes the N data samples evenly to m machines, performs separate minimization on each subset, and then averages the estimates. We provide a sharp analysis of this average mixture algorithm, showing t...

The continuous miniaturization of semiconductor devices imposes serious threats to design robustness against process variations and environmental fluctuations. Modern circuit designs may suffer from uncertain delays, not predictable in the design phase or even after manufacturing. This paper presents an optimization technique to make sequential circuits robust against delay variations and thus ...

Many applications utilize time and memory intensive simulations. We demonstrate a method of decreasing necessary space to 5% or less without losing time, compared to conventional ways, applicable to any statistical software. The idea is to perform simulations in portions. Statistics are calculated for each portion and stored with the current seed(s). After running all portions, the full set of ...