High-Dimensional Statistics

• Robust Training in High Dimensions via Block Coordinate Geometric Median Descent (arXiv, slides, poster, software, code)
  A. Acharya, A. Hashemi, P. Jain, S. Sanghavi, I. Dhillon, U. Topcu.
  In International Conference on Artificial Intelligence and Statistics (AISTATS), March 2022.
• A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution (arXiv, software, code)
  D. Inouye, E. Yang, G. Allen, P. Ravikumar.
  WIREs Computational Statistics , May 2017.
• Mixed Linear Regression with Multiple Components (pdf, software)
  K. Zhong, P. Jain, I. Dhillon.
  In Neural Information Processing Systems (NIPS), December 2016.
• Square Root Graphical Models: Multivariate Generalizations of Univariate Exponential Families that Permit Positive Dependencies (pdf, arXiv, poster, software, code)
  D. Inouye, P. Ravikumar, I. Dhillon.
  In International Conference on Machine Learning (ICML), pp. 2445-2453, June 2016. (Oral)
• A Convex Atomic-Norm Approach to Multiple Sequence Alignment and Motif Discovery (pdf, slides, poster, software)
  I. Yen, X. Lin, J. Zhang, P. Ravikumar, I. Dhillon.
  In International Conference on Machine Learning (ICML), June 2016. (Oral)
• Collaborative Filtering with Graph Information: Consistency and Scalable Methods (pdf, software, code)
  N. Rao, H. Yu, P. Ravikumar, I. Dhillon.
  In Neural Information Processing Systems (NIPS), pp. 2098–2106, December 2015. (Spotlight)
• Matrix Completion with Noisy Side Information (pdf, software)
  K. Chiang, C. Hsieh, I. Dhillon.
  In Neural Information Processing Systems (NIPS), pp. 3447–3455, December 2015. (Spotlight)
• A Convex Exemplar-based Approach to MAD-Bayes Dirichlet Process Mixture Models (pdf, slides, software)
  I. Yen, X. Lin, K. Zhong, P. Ravikumar, I. Dhillon.
  In International Conference on Machine Learning (ICML), pp. 2418-2426, July 2015.
• Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings (pdf, software)
  I. Yen, C. Hsieh, P. Ravikumar, I. Dhillon.
  In Neural Information Processing Systems (NIPS), pp. 1008-1016, December 2014.
• Elementary Estimators for Graphical Models (pdf, software)
  E. Yang, A. Lozano, P. Ravikumar.
  In Neural Information Processing Systems (NIPS), December 2014.
• Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs (pdf, poster, software, code)
  D. Inouye, P. Ravikumar, I. Dhillon.
  In Neural Information Processing Systems (NIPS), pp. 3158-3166, December 2014.
• QUIC: Quadratic Approximation for Sparse Inverse Covariance Matrix Estimation (pdf, software)
  C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
  Journal of Machine Learning Research (JMLR), October 2014.
• Elementary Estimators for High-Dimensional Linear Regression (pdf, software)
  E. Yang, A. Lozano, P. Ravikumar.
  In International Conference on Machine Learning (ICML), 2014. (Oral)
• Elementary Estimators for Sparse Covariance Matrices and other Structured Moments (pdf, software)
  E. Yang, A. Lozano, P. Ravikumar.
  In International Conference on Machine Learning (ICML), 2014. (Oral)
• Learning Graphs with a Few Hubs (pdf, software)
  R. Tandon, P. Ravikumar.
  In International Conference on Machine Learning (ICML), June 2014.
• Admixture of Poisson MRFs: A Topic Model with Word Dependencies (pdf, slides, poster, software, code)
  D. Inouye, P. Ravikumar, I. Dhillon.
  In International Conference on Machine Learning (ICML), pp. 683-691, June 2014.
• Mixed Graphical Models via Exponential Families (pdf, software)
  E. Yang, Y. Baker, P. Ravikumar, G. Allen, Z. Liu.
  In International Conference on Artificial Intelligence and Statistics (AISTATS), 2014. (Oral)
• Dirty Statistical Models (pdf, software)
  E. Yang, P. Ravikumar.
  In Neural Information Processing Systems (NIPS), 2013.
• Conditional Random Fields via Univariate Exponential Families (pdf, software)
  E. Yang, P. Ravikumar, G. Allen, Z. Liu.
  In Neural Information Processing Systems (NIPS), December 2013.
• On Poisson Graphical Models (pdf, software)
  E. Yang, P. Ravikumar, G. Allen, Z. Liu.
  In Neural Information Processing Systems (NIPS), December 2013.
• BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables (pdf, software)
  C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar, R. Poldrack.
  In Neural Information Processing Systems (NIPS), December 2013. (Oral)
• Large Scale Distributed Sparse Precision Estimation (pdf, software)
  H. Wang, C. Hsieh, A. Banerjee, P. Ravikumar, I. Dhillon.
  In Neural Information Processing Systems (NIPS), December 2013.
• On the Difficulty of Learning Power Law Graphical Models (pdf, software)
  R. Tandon, P. Ravikumar.
  In IEEE International Symposium on Information Theory (ISIT), 2013.
• A Dirty Model for Multiple Sparse Regression (pdf, software)
  A. Jalali, P. Ravikumar, S. Sanghavi.
  IEEE Transactions on Information Theory 59(12), pp. 7947-7968, 2013.
• On Robust Estimation of High Dimensional Generalized Linear Models (pdf, software)
  E. Yang, A. Tewari, P. Ravikumar.
  In International Joint Conference on Artificial Intelligence (IJCAI), 2013.
• A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation (pdf, software)
  C. Hsieh, I. Dhillon, P. Ravikumar, A. Banerjee.
  In Neural Information Processing Systems (NIPS), pp. 2339-2347, December 2012.
• Graphical Models via Generalized Linear Models (pdf, software)
  E. Yang, P. Ravikumar, G. Allen, Z. Liu.
  In Neural Information Processing Systems (NIPS), 2012. (Oral)
• A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers (pdf, software)
  S. Negahban, P. Ravikumar, M. Wainwright, B. Yu.
  Statistical Science 27(4), pp. 538-557, 2012.
• High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods (pdf, software)
  A. Jalali, C. Johnson, P. Ravikumar.
  In International Conference on Artificial Intelligence and Statistics (AISTATS), 2012.
• Greedy Algorithms for Structurally Constrained High Dimensional Problems (pdf, software)
  A. Tewari, P. Ravikumar, I. Dhillon.
  In Neural Information Processing Systems (NIPS), December 2011.
• Sparse Inverse Covariance Matrix Estimation using Quadratic Approximation (pdf, software)
  C. Hsieh, M. Sustik, I. Dhillon, P. Ravikumar.
  In Neural Information Processing Systems (NIPS), December 2011.
• High dimensional covariance estimation by minimizing l1-penalized log-determinant divergence (pdf, software)
  P. Ravikumar, M. Wainwright, B. Yu, G. Raskutti.
  Electronic Journal of Statistics (EJS) 5, pp. 935-980, 2011.
• On the Use of Variational Inference for Learning Discrete Graphical Models (pdf, software)
  E. Yang, P. Ravikumar.
  In International Conference on Machine Learning (ICML), 2011. (Oral)