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Natural language problems often demand new algorithms. The main challenges area combinatorially large discrete space of linguistic structures  a high-dimensional continuous space of statistical parameters  Many of our algorithmic papers give general solutions to some formal problem, and thus have multiple uses. I have occasionally proved hardness results.
Dynamic programming is extremely useful for analyzing sequence data. The papers below introduce novel dynamic programming algorithms (primarily for parsing and machine translation). Other cool papers, not listed below, show how dynamic programming algorithms can be embedded as efficient subroutines within variational inference (belief propagation), relaxation (dual decomposition, row generation), and large-neighborhood local search.
"Deep learning" usually refers to the use of multi-layer neural networks to model functions or probability distributions. The advantage of these highly parametric models is that they are expressive enough to fit a wide range of real-world phenomena. We have been particularly interested in combining deep learning with graphical models and other approaches to structured prediction, in order to marry the flexibility of deep learning with the insight of domain-specific modeling. Deep architectures for NLP typically include parameters for vector embeddings of words, which is an important subtopic.
My students and I often identify a pesky formal problem in statistical NLP or ML and try to give a general solution to it. The formal settings for our algorithms often involve finite-state machines, various kinds of grammars and synchronous grammars, and graphical models. These objects are usually equipped with real-valued weights that define a structured prediction problem (see here). One can treat a wider variety of problems by allowing the weights to be elements of an arbitrary semiring. Our work on weighted logic programming (see papers on the Dyna language) has led us to develop flexible algorithms for maintaining truth values in arithmetic circuits.
My students and I have worked in several ML settings, some of them novel. I have a relatively well-developed statistical philosophy, leading to the design of novel training objectives. We've also offered techniques for optimizing such objectives. Once upon a time, I was into neural networks and have started to use deep learning again.
We have introduced new machine learning ideas in several settings, including in unsupervised, supervised and semi-supervised learning; domain adaptation; structure learning for graphical models; hybrids of probabilistic modeling with deep learning; cost-aware learning; reinforcement learning; and creative use of annotators.
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