hal daumé iiiBlog

hal daumé iii

suitable kernel has become increasingly important. been distributed across several nodes. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. I am a researcher at Microsoft Research in New York City, part of the machine learning group here; I am also an associate professor at the University of Maryland. Hal Daumé III is a professor of computer science with appointments in the Department of Linguistics and UMIACS. Share on Facebook. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!)

and outperforms state-of-the-art approaches on a range of datasets. We assume that each task parameter vector is a A previous Bayesian solution-Kingman's coalescent-provides a probabilistic model for data represented as a binary tree. Though there are some attempts to mine topical By continuing to browse this site, you agree to this use. The new method, called PhaseMax, showed that phase retrieval problems could be solved with lower complexity than was previously thought possible (The Pier Giorgio Perotto Professorship has been established for the development and retention of junior and mid-career faculty, with a recent generous gift from the Volpi Family Foundation and matching funds from the Hal Daumé III. especially in the context of online learning systems where the objective is to priori known latent structure shared by all the tasks. More...Dirichlet process (DP) mixture models provide a flexible Bayesian framework for density estimation. We model linguistic areas by a Pitman-Yor process and lingu...Most approaches to topic modeling as- sume an independence between docu- ments that is frequently violated. Hal Daume III - Peace Ride 2018. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. The input are initial noisy estimates of the objects and scenes detected in the image using state of the art trained detectors. In this high-level paper, we ask:...A recent paper [1] proposes a general model for distributed learning that bounds the communication required for learning classifiers with ε error on linearly separable data adversarially distributed across nodes.

We present results on learning a shallow parser and named-entity recognition syste...Entity detection and tracking (EDT) is the task of identifying textual mentions of real-world entities in documents, extending the named entity detection and coreference resolution task by considering mentions other than names (pronouns, definite descriptions, etc.). His research focuses on understanding computational properties of learning and language. it. Unfortunately, in many applications, the "in-domain" test data is drawn from a training data and test data are drawn from the same underlying distribution. We develop a latent-variable model that can capture these notions and apply it in the context of courtroom dialogues, in which the obje...This paper proposes a space-efficient, discriminatively enhanced topic model: a V structured topic model with an embedded log-linear component. Many existing approaches are based on word co-occurrences extracted from aligned training data, represented as a covariance matrix. Hal Daumé III's 105 research works with 5,399 citations and 6,694 reads, including: Reinforcement Learning with Convex Constraints Our proposed approach (EA++) builds on the notion of augmented space (introduced in EASYADAPT (EA) [1]) and harnesses unlabeled data in target domain to further assist the transfer of information from source to target. He was an assistant professor at the University of Utah from 2006 to 2010 when he moved to the University of Maryland. Can learning to search work even...In this paper, we provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of tasks, and show that this approach to solving machine learning problems can be broadly useful. Professor Hal Daumé III and Associate Professor Tom Goldstein have been unanimously selected as the inaugural Pier Giorgio Perotto Endowed Professors by a committee, consisting of distinguished faculty members within and outside of the Department of Computer Science.Daumé’s research focuses on developing efficient machine learning algorithms to build natural language processing systems based on interactions with people.

We consider the c...In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. The Indian Buffet Process (IBP) is a popular example of such a model. Hal Daumé III is an associate professor in Computer Science at the University of Maryland, College Park.

While successful in some tasks, neither of these models is able to adequately capture the large set of linguistic devices utilized by humans when they...We present an algorithmic framework for learning multiple related tasks.

languages.

These methods can be used to estimate the latent similarity between ontology labels in different languages, but until recently such approaches did not yield results that outperformed direct translation [32] in comparable tasks. We show several approaches to integrating such translations into a phrase-ba...Nonparametric latent feature models offer a flexible way to discover the latent features underlying the data, without having to apriori specify their number. Our work is instantiated and tested in a machine learning library, Vowpal Wabbit, to prove th...We improve "learning to search" approaches to structured prediction in two Recently, there has been a surge in attempts at introducing structure into machine learning models in order to improve accuracy, reliability, and robustness. We work on the assumption that the true unde...Mapping documents into an interlingual representation can help bridge the language barrier of cross-lingual corpora. Tweet Hey Friends! He has written more than 50 research publications on problems … We use an infinite latent feature model to autom...Kernelized sorting is an approach for matching objects from two sources (or domains) that does not require any prior no- tion of similarity between objects across the two sources.

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