• Efficient Distributed Learning with Sparsity [arxiv]
    J. Wang, M. Kolar, N. Srebro, T. Zhang
  • Distributed Multi-Task Learning with Shared Representation [arxiv]
    J. Wang, M. Kolar, N. Srebro
  • Post-Regularization Confidence Bands for High Dimensional Nonparametric Models with Local Sparsity [arxiv]
    J. Lu, M. Kolar, H. Liu
  • ROCKET: Robust Confidence Intervals via Kendall's Tau for Transelliptical Graphical Models [pdf][supplement]
    R. Foygel Barber, M. Kolar
  • A General Framework for Robust Testing and Confidence Regions in High-Dimensional Quantile Regression [arxiv]
    T. Zhao, M. Kolar, H. Liu
  • Inference for Sparse Conditional Precision Matrices [arxiv]
    J. Wang, M. Kolar
  • Mean and variance estimation in high-dimensional heteroscedastic models with non-convex penalties [arxiv]
    J. Sharpnack, M. Kolar

  • Sparsistent Estimation of Time-Varying Discrete Markov Random Fields. June 2009. [arXiv]
    M. Kolar and E. P. Xing. 
  • Recovering Block-structured Activations Using Compressive Measurements. 2012.  [pdf]
    S. Balakrishnan, 
    M. Kolar
    , A. Rinaldo and A. Singh. 

Refereed Publications


  • Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data [arxiv]
    J. Wang, J. D. Lee, M. Mahdavi, M. Kolar, N. Srebro


  • Statistical Inference for Pairwise Graphical Models Using Score Matching
    M. Yu, V. Gupta, M. Kolar
    NIPS [pdf]
  • Comment: Coauthorship and Citation Networks for Statisticians
    M. Kolar, M. Taddy
    AOAS [pdf]
  • Inference for High-dimensional Exponential Family Graphical Models
    J. Wang, M. Kolar
    AIStats [pdf]
  • Distributed Multi-Task Learning
    J. Wang, M. Kolar, N. Srebro
    AIStats [pdf]


  • Learning Structured Densities via Infinite Dimensional Exponential Families
    S. Sun, M. Kolar, J. Xu
    NIPS [pdf]
  • Optimal variable selection in multi-group sparse discriminant analysis
    I. Gaynanova, M. Kolar
    EJS [pdf]


  • Optimal Feature Selection in High-Dimensional Discriminant Analysis.
    M. Kolar
    , H. Liu. 
    IEEE IT. [arXiv] [journal]
  • Berry-Esseen bounds for estimating undirected graphs.
    L. Wasserman, M. Kolar, A. Rinaldo. 
    EJS [journal] [arXiv
  • Graph Estimation From Multi-Attribute Data.
    M. Kolar
    , H. Liu, E.P. Xing. 
    JMLR [journal] [arxiv]
  • High-Dimensional Sparse Structured Input-Output Models, with Applications to GWAS.
    E.P. Xing, M. Kolar, S. Kim, X. Chen. 
    Practical Applications of Sparse Modeling (MIT Press) [link]


  • Markov Network Estimation From Multi-attribute Data.
    M. Kolar
    , H. Liu, E.P. Xing. 
    ICML [pdf]
  • Feature Selection in High-Dimensional Classification.
    M. Kolar
    , H. Liu. 

    ICML [pdf]


  • M. Kolar, E.P. Xing. Estimating Networks with Jumps.
    EJS [pdf]
  • M. Kolar, J. Sharpnack. Variance Function Estimation in High-dimensions.
    ICML [pdf] [arxiv]
  • M. Kolar, E.P. Xing. Consistent Covariance Selection From Data With Missing Values.
    ICML [pdf]
  • M. Kolar, H. Liu. Marginal Regression for Multitask Learning.
    AIStats (oral presentation) [pdf] [supplementary]


  • S. Balakrishnan, M. Kolar, A. Rinaldo, A. Singh, and L. Wasserman. Statistical and computational tradeoffs in biclustering.
    NIPS – Computational Trade-offs in Statistical Learning. [pdf]
  • M. Kolar, S. Balakrishnan, A. Rinaldo, and A. Singh. Minimax Localization of Structural Information in Large Noisy Matrices.
    NIPS [pdf]
  • M. Kolar, E.P. Xing. On Time Varying Undirected Graphs.
    AIStats [pdf]


  • M. Kolar
    , J. Lafferty and L. Wasserman. Union Support Recovery in Multi-task Learning. 
    Journal of Machine Learning, 2010. [pdf]
  • M. Kolar
    , A. Parikh and E.P. Xing. On Sparse Conditional Covariance Selection. ICML 2010. [pdf]
  • M. Kolar, E.P. Xing. Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach. AIStats 2010. [pdf]
  • M. Kolar, L. Song, A. Ahmed, and E. P. Xing. Estimating time-varying networks. Annals of Applied Statistics, 2010. AOAS [pdf]


  • L. Song, M. Kolar, E. P. Xing. Time-Varying Dynamic Bayesian Networks. Advances in Neural Information Processing Systems 23, 2009.
  • M. Kolar, L. Song, E. P. Xing. Sparsistent Learning of Varying-coefficient Models with Structural Changes. Advances in Neural Information Processing Systems 23, 2009
  • L. Song, M. Kolar, and E. P. Xing. KELLER: Estimating time-evolving interactions between genes. The Sixteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2009). Bioinformatics 2009 25(12):i128-i136.


  • M. Kolar and E. P. Xing. Time varying ising models. NIPS 2008 Workshop on Analyzing Graphs: Theory and Applications, 2008
  • P. Ray, S. Shringarpure, M. Kolar and E. P. Xing. CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing. PLoS Computational Biology (2008), Vol 4 (6), June 2008

Undergraduate Work

  • S. Petrovic, B. Dalbelo Basic, J. Snajder, M. Kolar. Comparison of Collocation Extraction Measures for Document Indexing. Journal of Computing and Information Technology CIT 14 (2006), 4, (best student papers, ITI 2006)
  • M. Kolar, I. Vukmirovic, B. Dalbelo Basic, J. Snajder. Computer-Aided document Indexing Systems. Journal of Computing and Information Technology - CIT. 13 (2005), 4; 299-305, (awarded with the “SCIENCE” award)

Technical reports

  • M. Kolar and E. P. Xing. Improved Estimation of High-dimensional Ising Models. Technical report, 2008. arXiv