129 results on '"Guy Lebanon"'
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2. Local Context Sparse Coding.
3. Estimating Temporal Dynamics of Human Emotions.
4. Smooth sparse coding via marginal regression for learning sparse representations.
5. LLORMA: Local Low-Rank Matrix Approximation.
6. Local collaborative ranking.
7. Composite Statistical Inference for Semantic Segmentation.
8. A Comparative Study of Social Media and Traditional Polling in the Egyptian Uprising of 2011.
9. Beyond Sentiment: The Manifold of Human Emotions.
10. Ultrahigh Dimensional Feature Screening via RKHS Embeddings.
11. Learning multiple-question decision trees for cold-start recommendation.
12. Smooth Sparse Coding via Marginal Regression for Learning Sparse Representations.
13. Local Low-Rank Matrix Approximation.
14. Automatic Feature Induction for Stagewise Collaborative Filtering.
15. Chebyshev approximations to the histogram χ2 kernel.
16. Fast bregman divergence NMF using taylor expansion and coordinate descent.
17. Local Space-Time Smoothing for Version Controlled Documents.
18. Dimensionality Reduction for Text using Domain Knowledge.
19. Visualizing differences in web search algorithms using the expected weighted hoeffding distance.
20. Asymptotic Analysis of Generative Semi-Supervised Learning.
21. Statistical Estimation of Word Acquisition with Application to Readability Prediction.
22. Domain Knowledge Uncertainty and Probabilistic Parameter Constraints.
23. Determining Placement of Intrusion Detectors for a Distributed Application through Bayesian Network Modeling.
24. ARUBA: A Risk-Utility-Based Algorithm for Data Disclosure.
25. Local likelihood modeling of temporal text streams.
26. Non-parametric Modeling of Partially Ranked Data.
27. Statistical Translation, Heat Kernels and Expected Distances.
28. Isotonic Conditional Random Fields and Local Sentiment Flow.
29. Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk.
30. An Extended Cencov-Campbell Characterization of Conditional Information Geometry.
31. Learning Riemannian Metrics.
32. Preface.
33. Information Diffusion Kernels.
34. Conditional Models on the Ranking Poset.
35. Cranking: Combining Rankings Using Conditional Probability Models on Permutations.
36. Designing Moiré Patterns.
37. Boosting and Maximum Likelihood for Exponential Models.
38. PREA: personalized recommendation algorithms toolkit.
39. Unsupervised Supervised Learning II: Margin-Based Classification without Labels.
40. Estimating Probabilities in Recommendation Systems.
41. Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels.
42. Stochastic Composite Likelihood.
43. Statistical and Computational Tradeoffs in Stochastic Composite Likelihood.
44. Data transformations and representations for computation and visualization.
45. Beyond k-Anonymity: A Decision Theoretic Framework for Assessing Privacy Risk.
46. Generalized isotonic conditional random fields.
47. Visualizing Incomplete and Partially Ranked Data.
48. Sequential Document Visualization.
49. The Locally Weighted Bag of Words Framework for Document Representation.
50. Metric Learning for Text Documents.
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