- 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
- Optimal variable selection in multi-group sparse discriminant analysis [arxiv]
I. Gaynanova, 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. [arXiv
, A. Rinaldo and A. Singh.
Optimal Feature Selection in High-Dimensional Discriminant Analysis.
, H. Liu.
IEEE IT. [arXiv
Berry-Esseen bounds for estimating undirected graphs. L. Wasserman, M. Kolar, A. Rinaldo.
Graph Estimation From Multi-Attribute Data.
, H. Liu, E.P. Xing.
- 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.
, H. Liu, E.P. Xing.
Feature Selection in High-Dimensional Classification.
, H. Liu.
- M. Kolar, E.P. Xing. Estimating Networks with Jumps.
- 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.
- 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.
- M. Kolar, E.P. Xing. On Time Varying Undirected Graphs.
, J. Lafferty and L. Wasserman. Union Support Recovery in Multi-task Learning.
Journal of Machine Learning, 2010. [pdf
, A. Parikh and E.P. Xing. On Sparse Conditional Covariance Selection. ICML 2010. [pdf
, E.P. Xing. Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach. AIStats 2010. [pdf
, 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
- 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)
- M. Kolar and E. P. Xing. Improved Estimation of High-dimensional Ising Models. Technical report, 2008. arXiv