Ben taskar phd thesis topics

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My primary research interests are machine learning, computational linguistics and computer vision. I received my bachelor's and doctoral degree in Computer Science from Stanford University. Our survey paper on determinantal processes was just published phd thesis topics Ben taskar phd thesis topics and Trends arXiv version.

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thesis topics Among many remarkable properties, they offer tractable algorithms for exact inference, including computing marginals, computing certain conditional thesis topics, and sampling. DPPs are a natural model for subset selection problems where diversity is preferred. For example, they phd thesis topics be used to select diverse sets of sentences read article form document summaries, or to return phd thesis topics but varied text and image search results, or thesis topics detect non-overlapping phd thesis topics object ben taskar in video.

In our recent work, we discovered a novel factorization and dual representation of DPPs that enables efficient inference for exponentially-sized structured sets. We developed a new inference algorithm based on Newton identities for DPPs conditioned on subset size.

We also derived efficient parameter estimation for DPPs from several types of observations. Ben taskar phd thesis topics demonstrated the advantages of the model on several natural language and vision tasks: DPP ben taskar phd thesis topics Computation and Approximation in Structured Prediction Structured prediction tasks pose a fundamental bias-computation phd thesis topics The need for complex models to increase predictive power on the one hand and the limited computational resources for inference in the exponentially-sized output spaces on the other.

We formulate and develop structured prediction cascades to address this trade-off: We represent an exponentially large set of filtered outputs using max marginals and propose a novel convex loss for learning cascades that balances filtering error with filtering efficiency.

We derive generalization bounds for error and efficiency losses and evaluate our approach on several natural language and vision problems: We find that the learned cascades are capable of reducing the complexity of inference by up ben taskar phd several orders of magnitude, enabling the use of models which incorporate click to see more order dependencies and features and yield significantly higher accuracy.

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Posterior Regularization for Structured Latent Ben taskar article source thesis topics Models Posterior regularization is a ben taskar phd thesis topics framework for structured, weakly supervised learning. Our framework efficiently incorporates indirect supervision via constraints on posterior distributions of ben taskar phd thesis topics models with latent variables.

Posterior regularization separates model complexity from the complexity of structural constraints it is desired to satisfy.

By directly imposing decomposable regularization on the posterior moments of latent variables during learning, we retain the computational efficiency of the unconstrained model while ensuring desired constraints hold in expectation.

We present an efficient algorithm for learning ben taskar posterior regularization and illustrate computer architecture research papers versatility on a diverse set of structural constraints such as bijectivity, symmetry and group sparsity in several large scale experiments, including multi-view learning, cross-lingual dependency grammar phd thesis, unsupervised part-of-speech induction, and bitext word alignment.

Our setting is ben taskar phd thesis topics by a common topics in many topics and video collections, where only partial access to labels is available.

The goal is to learn a classifier that can disambiguate ben taskar partially-labeled training instances, and generalize to unseen data. Ben taskar phd thesis topics define an intuitive property of the data distribution that sharply characterizes the ability to learn in this setting and show that effective learning is possible even when ben taskar the data is only partially labeled.

Ben taskar phd thesis topics this property of the phd thesis topics, we propose a convex learning formulation based on minimization of a loss function appropriate for phd thesis topics partial label setting.

Phd thesis in heat and mass transfer

We analyze the conditions under which our loss function phd thesis asymptotically consistent, as well as its generalization and transductive performance. We apply our framework to identifying topics culled from web ben taskar sources and to naming characters in TV series and movies; in particular, we annotated and experimented on a very large video data set and achieve very accurate character naming on ben taskar phd thesis topics a dozen episodes of the TV series Lost.

Ben taskar phd thesis topics from One ExampleB. Sappand B. Kuleszaand B. Foundations and Trends in Machine Learning: Determinantal Point Processes with A.

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Structured Prediction CascadesD. Gracaand B. Learning Determinantal Point ProcessesA. For updated results on the summarization task DUC04see the long arXiv report. Learning phd here Partial LabelsT. Weissand B. Pereiraand B. Ganchevand B. Mordohaiand B.

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