Selected publications

Submitted, in press, or in revision
  1. Regularized sliced inverse regression for kernel models.
  2. Estimating variable structure and dependence in Multi-task learning via gradients .
  3. Learning gradients: predictive models that infer geometry and dependence.
  4. Non-parametric Bayesian kernel models.
  5. Learning Gradients and Feature Selection on Manifolds.
    Computational Biology
  1. Modeling Cancer Progression via Pathway Dependencies PLoS Comp. Bio.
  2. Gene Expression Programs of Human Smooth Muscle Cells: Tissue-Specific Differentiation and Prognostic Significance in Breast Cancers, PLoS Genetics.
  3. Genomic sweeping for hypermethylated genes, Bioinformatics.
  4. Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation, PLoS Comp. Bio.
  5. Analysis of Sample Set Enrichment Scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles, Bioinformatics.
  6. Gene expression changes and moelcular pathways mediating activity-dependent plasticity in visual cortex, Nature Neuroscience.
  7. A Genomic Strategy to Refine Prognosis in Early Stage Non-Small Cell Lung Carcinoma, N Eng J Med.
  8. Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-wide Expression Profiles, PNAS.
  9. Commentary on paper: Application of a priori established gene sets to discover biologically important differential expression in microarray data, PNAS.
  10. Androgen-Induced Differentiation and Tumorigenicity of Human Prostate Epithelial Cells, Cancer Research.
  11. An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis, Nature Genetics.
  12. Gene Selection via a Spectral Approach, IEEE Workshop on Computer Vision Methods for Bioinformatics.
  13. Estimating Dataset Size Requirements for Classifying DNA Microarray Data, Journal Computational Biology.
  14. An Analytical Method for Multi-class Molecular Cancer Classification, SIAM Reviews.
  15. Optimal gene expression analysis by microarrays, Cancer Cell.
  16. A Uniform Approach to Molecular Cancer Diagnosis Using Tumor Gene Expression Signatures, PNAS.
  17. Classifying Microarray Data Using Support Vector Machines, Understanding and Using Microarray Analysis Techniques: A Practical Guide.
  18. Gene Expression-Based Classification and Outcome Prediction of Central Nervous System Embryonal Tumors, Nature.
  19. Molecular classification of multiple tumor types, Bioinformatics.
  20. Support Vector Machine Classification of Microarray Data, CBCL/AI Memo.

Statistical learning
  1. Characterizing the function space for Bayesian kernel models, JMLR.
  2. Understanding the use of unlabelled data in predictive modelling, Statistical Science.
  3. Estimation of Gradients and Coordinate Covariation in Classification, JMLR.
  4. Learning Coordinate Covariances via Gradients, JMLR.
  5. Stability Results in Learning Theory, Analysis and Applications.
  6. Permutation Tests for Classification, Computational Learning Theory.
  7. Risk Bounds for Mixture Density Estimation, ESAIM: Probability and Statistics.
  8. Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization, Advances in Computational Mathematics.
  9. Learning Theory: general conditions for predictivity, Nature.
  10. Comentary on paper: Learning theory: Past performance and future results , Nature.
  11. Regression and Classification with Regularization, Nonlinear Estimation and Classification.
  12. B, Uncertainty in Geometric Computations.
  13. Bounds on sample size for policy evaluation in Markov environments, Computational Learning Theory.
  14. Feature Selection for SVMs, Neural Information Processing Systems.
  15. Choosing Multiple Parameters for Support Vector Machines, Machine Learning.
  16. Support Vector Method for Multivariate Density Estimation, CBCL/AI Memo.
  17. On the Noise Model of Support Vector Machines Regression, Algorithmic Learning Theory.