David S. Rosenberg

IJCAI Conference 2015 Conference Paper

Collaborative Place Models

• Berk Kapicioglu
• David S. Rosenberg
• Robert E. Schapire
• Tony Jebara

ICML Conference 2008 Conference Paper

An RKHS for multi-view learning and manifold co-regularization

• Vikas Sindhwani
• David S. Rosenberg

Inspired by co-training, many multi-view semi-supervised kernel methods implement the following idea: find a function in each of multiple Reproducing Kernel Hilbert Spaces (RKHSs) such that (a) the chosen functions make similar predictions on unlabeled examples, and (b) the average prediction given by the chosen functions performs well on labeled examples. In this paper, we construct a single RKHS with a data-dependent "co-regularization" norm that reduces these approaches to standard supervised learning. The reproducing kernel for this RKHS can be explicitly derived and plugged into any kernel method, greatly extending the theoretical and algorithmic scope of coregularization. In particular, with this development, the Rademacher complexity bound for co-regularization given in (Rosenberg & Bartlett, 2007) follows easily from wellknown results. Furthermore, more refined bounds given by localized Rademacher complexity can also be easily applied. We propose a co-regularization based algorithmic alternative to manifold regularization (Belkin et al., 2006; Sindhwani et al., 2005a) that leads to major empirical improvements on semi-supervised tasks. Unlike the recently proposed transductive approach of (Yu et al., 2008), our RKHS formulation is truly semi-supervised and naturally extends to unseen test data.

UAI Conference 2007 Conference Paper

Mixture-of-Parents Maximum Entropy Markov Models

• David S. Rosenberg
• Dan Klein 0001
• Ben Taskar

We present the mixture-of-parents maximum entropy Markov model (MoP-MEMM), a class of directed graphical models extending MEMMs. The MoP-MEMM allows tractable incorporation of long-range dependencies be- tween nodes by restricting the conditional distribution of each node to be a mixture of distributions given the parents. We show how to efficiently compute the exact marginal posterior node distributions, regardless of the range of the dependencies. This enables us to model non-sequential correlations present within text documents, as well as between in- terconnected documents, such as hyperlinked web pages. We apply the MoP-MEMM to a named entity recognition task and a web page classification task. In each, our model shows significant improvement over the basic MEMM, and is competitive with other long- range sequence models that use approximate inference.