Optimization and Statistics

  • Hyebin Song, Garvesh Raskutti "PUlasso: High-dimensional variable selection with presence-only data", Journal of the American Statistical Association, 2018. Paper

  • Han Chen, Garvesh Raskutti, Ming Yuan "Non-Convex Projected Gradient Descent for Generalized Low-Rank Tensor Regression." Paper

  • Garveh Raskutti, Michael Mahoney, "A statistical perspective on randomized sketching for ordinary least-squares", Journal of Machine Learning Research, 2017 (Preliminary version in ICML 2015). Paper

  • Garveh Raskutti, Sayan Mukherjee, "The Information Geometry of Mirror Descent", IEEE Transactions on Information Thoery, 2015. Paper

  • Garveh Raskutti, Martin Wainwright, Bin Yu "Early Stopping and Non-parametric Regression: An optimal data-dependent setopping rule", Journal of Machine Learning Research, 2014. Paper

    • Time Series Networks

  • Lili Zheng, Garvesh Raskutti "Testing for high-dimensional network parameters in auto-regressive models." Paper

  • Benjamin Mark, Garvesh Raskutti, Rebecca Willett "Estimating Network Structure from Incomplete Event Data." Paper

  • Benjamin Mark, Garvesh Raskutti, Rebecca Willett "Network Estimation from Point Process Data", to appear in IEEE Transactions on Information Theory. Paper

  • Eric Hall, Garvesh Raskutti, Rebecca Willett "High-dimensional Autoregressive Generalized Linear Models", to appear in IEEE Transactions on Information Theory. Paper

  • Hao Zhou, Garvesh Raskutti "Non-parametric sparse additive network models", IEEE Transactions on Information Theory, 2018. Paper

    • High-dimensional Regression

  • Yuan Li, Garvesh Raskutti, "Minimax optimal convex methods for Poisson inverse problems under $\ell_q$-ball sparsity", IEEE Transactions on Information Theory, 2018. Paper

  • Garvesh Raskutti, Ming Yuan "Convex Regularization for High-dimensional tensor regression", Annals of Statistics, 2018. Paper

  • Xin Jiang, Garvesh Raskutti, Rebecca Willett "Minimax Optimal Rates for Poisson Inverse Problems under Physical Constraints", IEEE Transactions on Information Theory, 2015. Paper

  • Garvesh Raskutti, Martin Wainwright, Bin Yu "Minimax Optimal Rates for High-dimensional Sparse Additive Models over Kernel Classes", Journal of Machine Learning Research, 2012. Paper

  • Garvesh Raskutti, Martin Wainwright, Bin Yu "Minimax Rates for high-dimensional linear regression over l_q-balls", IEEE Transactions on Information Theory, 2011. Paper

  • Garvesh Raskutti, Martin Wainwright, Bin Yu "Restricted Eigenvalue Condition for Correlated Gaussian Design", Journal of Machine Learning Research, 2010. Paper

    • Graphical Models

  • Gunwoong Park, Garveh Raskutti, "Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring", Journal of Machine Learning Research, 2018 (Preliminary version in NIPS 2015). Paper

  • Garveh Raskutti, Caroline Uhler "Learning DAGs based on Sparsest Permutations", STAT, 2018. Paper

  • Gunwoong Park, Garveh Raskutti, "Identifiability assumptions for directed graphical models with feedback". Paper

  • Malcolm Forster, Garveh Raskutti, Reuben Stern, Naftali Weinberger "The Frugal Inference of Causal Relations", To appear in British Jornal of Philosophy of Science, 2016. Paper

  • Caroline Uhler, Garveh Raskutti, Peter Buhlmann, Bin Yu "Geometry of Faithfulness Assumption in causal inference", Annals of Statistics, 2013. Paper

  • Pradeep Ravikumar, Martin Wainwright, Garveh Raskutti, Bin Yu "High-dimensional covariance estimation by minimizing l_1-penalized log-determinant divergence", Electrong Journal of Statistics, 2011. Paper

    • Optical Network Design

  • Kerry Hinton, Garveh Raskutti, Peter Farrell, Rodney Tucker, "Switching Energy and Device Size Limitations for Photonic Signal Processing", IEEE Journal of Selected Topics in Quantum Electronics, 2009. Paper

  • Eric Wong, Jayant Baliga, Moshe Zukerman, Andrew Zalesky, Garveh Raskutti "A New Method for Blocking Probability Evaluation in OBS/OPS Networks with Deflection Routing", IEEE Journal of Lightwave Technology, 2009. Paper

  • Rodney Tucker, Kerry Hinton, Garveh Raskutti "Energy consumption limits in high-speed optical and electronic signal processing", Electronics Letters, 2007. Paper

  • Garveh Raskutti, Andrew Zalesky, Eric Wong, Moshe Zukerman "Enhanced Blocking Probability Evaluation for Circuit-switched trunk reservation networks", IEEE Communications Letters, 2007.

    Other

  • Si Wang, Weihing Guo, Ting-zhu Huang, Garvesh Raskutti "Image Inpainting using Reproducing Kernel Hilbert Space and Heaviside Functions", Journal of Computational and Applied Mathematics, 2016.

  • Adam Kowalczyk, Danielle Greenawalt, Justin Bedo, Cuong Duong, Garvesh Raskutti, Robert Thomas, Wayne Phillips "Large Validation of Anti-learnable Signature in Classification of Response to Chemoradiotherapy in Esophageal Adenocarcinoma Patients", Optimization and Systems Biology, 2007.

  • Cuong Duong, Danielle Greenawalt, Adam Kowalczyk, Marianne Ciavarella, Garvesh Raskutti, William Murray, Wayne Phillips, Robert Thomas "Pretreatment Gene Expression Profiles can be used to Predict Response to Neoadjuvant Chemoradiotherapy in Esopageal Cancer", Annals of Surgical Oncology, 2017.