1.4k citations · 4.5k across the 58 of their papers we have counts for
8 papers · 2 filters
The Infinite Hierarchical Factor Regression Model
Piyush Rai, Hal Daumé
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we pro…
Frustratingly Easy Domain Adaptation
Hal Daumé
We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough ``target'' data to do slightly better than just using only ``source'' data.…
Fast search for Dirichlet process mixture models
Hal Daumé
Dirichlet process (DP) mixture models provide a flexible Bayesian framework for density estimation. Unfortunately, their flexibility comes at a cost: inference in DP mixture models…
Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction
Hal Daumé, Daniel Marcu
Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., lin…
A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior
Hal Daumé, Daniel Marcu
We develop a Bayesian framework for tackling the supervised clustering problem, the generic problem encountered in tasks such as reference matching, coreference resolution, identit…
Cross-Task Knowledge-Constrained Self Training
Hal Daumé
We present an algorithmic framework for learning multiple related tasks. Our framework exploits a form of prior knowledge that relates the output spaces of these tasks. We present…