This work considers a computationally and statistically efficient parameter estimation method
for a wide class of latent variable models—including Gaussian mixture models, hidden …

Many real-world problems involve the optimization of multiple, possibly conflicting
objectives. Multi-objective reinforcement learning (MORL) is a generalization of standard …

In recent years we have seen the development of efficient provably correct algorithms for
learning Weighted Finite Automata (WFA). Most of these algorithms avoid the known …

Many tasks in text and speech processing and computational biology require estimating
functions map** strings to real numbers. A broad class of such functions can be defined …

Spectral Learning of Latent-Variable PCFGs Page 1 Edinburgh Research Explorer Spectral
Learning of Latent-Variable PCFGs Citation for published version: Cohen, SB, Stratos, K, Collins …

Abstract Latent-variable PCFGs (L-PCFGs) are a highly successful model for natural
language parsing. Recent work (Cohen et al., 2012) has introduced a spectral algorithm for …

Probabilistic latent-variable models are a powerful tool for modelling structured data.
However, traditional expectation-maximization methods of learning such models are both …

Spectral learning methods have recently been proposed as alternatives to slow, non-convex
optimization algorithms like EM for a variety of probabilistic models in which hidden …

This paper explores unsupervised learning of parsing models along two directions. First,
which models are identifiable from infinite data? We use a general technique for numerically …

This paper re-visits the spectral method for learning latent variable models defined in terms
of observable operators. We give a new perspective on the method, showing that operators …