Publications

2016
 		A Comprehensive Approach to Mode Clustering.
Yen-Chi Chen, Christopher R. Genovese, and Larry Wasserman.
Electronic Journal of Statistics 10(1).
Nonparametric Modal Regression.
Yen-Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani, and Larry Wasserman.
The Annals of Statistics 44(2).
Statistical Inference for Cluster Trees.
Yen-Chi Chen, Jisu Kim, Sivaraman Balakrishnan, Alessandro Rinaldo, and Larry Wasserman.
Preprint.
Active Learning Algorithms for Graphical Model Selection.
Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong H. Park.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS'16).
Finding Singular Features.
Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, and Larry Wasserman.
Preprint.
Exact Post-Selection Inference for Changepoint Detection and Other Generalized Lasso Problems.
Sangwon Hyun, Max G'Sell, and Ryan Tibshirani.
Preprint.
Minimax Rates for Estimating the Dimension of a Manifold.
Jisu Kim, Alessandro Rinaldo, and Larry Wasserman.
Preprint.
Distribution-Free Predictive Inference for Regression.
Jing Lei, Max G'Sell, Alessandro Rinaldo, Ryan Tibshirani, and Larry Wasserman.
Preprint.
Minimax Lower Bounds for Linear Independence Testing.
Aaditya Ramdas, David Isenberg, Aarti Singh, and Larry Wasserman.
IEEE International Symposium on Information Theory (ISIT'16).
Graph Sparsification Approaches for Laplacian Smoothing.
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS'16).
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers.
Veeranjaneyulu Sadhanala, Yu-Xiang Wang, and Ryan Tibshirani.
Preprint.
Minimax Subsampling for Estimation and Prediction in Low-Dimensional Linear Regression.
Yining Wang and Aarti Singh.
Preprint.
Noise-Adaptive Margin-Based Active Learning and Lower Bounds under Tsybakov Noise Condition.
Yining Wang and Aarti Singh.
Proceedings of the 30th AAAI Conference on Aritificial Intelligence (AAAI'16).
Graph Connectivity in Noisy Sparse Subspace Clustering.
Yining Wang, Yu-Xiang Wang, and Aarti Singh.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS'16).
2015
 		Risk Bounds For Mode Clustering.
Martin Azizyan, Yen-Chi Chen, Aarti Singh, and Larry Wasserman.
Preprint.
Extreme Compressive Sampling for Covariance Estimation.
Martin Azizyan, Akshay Krishnamurthy, and Aarti Singh.
Preprint.
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures.
Martin Azizyan, Aarti Singh, and Larry Wasserman.
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15).
Asymptotic Theory for Density Ridges.
Yen-Chi Chen, Christopher R. Genovese, and Larry Wasserman.
The Annals of Statistics 43(5).
Density Level Sets: Asymptotics, Inference, and Visualization.
Yen-Chi Chen, Christopher R. Genovese, and Larry Wasserman.
Preprint.
Optimal Ridge Detection using Coverage Risk.
Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, and Larry Wasserman.
Advances in Neural Information Processing Systems 28 (NIPS'15).
Cosmic Web Reconstruction through Density Ridges: Catalogue.
Yen-Chi Chen, Shirley Ho, Jon Brinkmann, Peter E. Freeman, Christopher R. Genovese, Donald P. Schneider, Larry Wasserman.
Preprint.
Statistical Analysis of Persistence Intensity Functions.
Yen-Chi Chen, Daren Wang, Alessandro Rinaldo, and Larry Wasserman.
Preprint.
Statistical Inference using the Morse-Smale Complex.
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman.
Preprint.
Selective Sequential Model Selection.
William Fithian, Jonathan Taylor, Robert Tibshirani, and Ryan Tibshirani.
Preprint.
Efficient Contextual Semi-Bandit Learning.
Akshay Krishnamurthy, Alekh Agarwal, and Miroslav Dudik.
arXiv:1502.05890.
On Estimating $L_2^2$ Divergence.
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, and Larry Wasserman.
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15).
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing.
Aaditya Ramdas, Sashank Reddi, Barnabas Poczos, Aarti Singh, and Larry Wasserman.
Preprint.
On the Decreasing Power of Kernel- and Distance-Based Nonparametric Hypothesis Tests in High Dimensions.
Aaditya Ramdas, Sashank Reddi, Barnabas Poczos, Aarti Singh, and Larry Wasserman.
Proceedings of the 29th AAAI Conference on Aritificial Intelligence (AAAI'15).
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives.
Sashank Reddi, Aaditya Ramdas, Barnabas Poczos, Aarti Singh, and Larry Wasserman.
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15).
Column Subset Selection with Missing Data via Active Sampling.
Yining Wang and Aarti Singh.
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15).
An Empirical Comparison of Sampling Techniques for Matrix Column Subset Selection.
Yining Wang and Aarti Singh.
Allerton Conference on Communication, Control and Computing.
Provably Correct Active Sampling Algorithms for Matrix Column Subset Selection with Missing Data.
Yining Wang and Aarti Singh.
Preprint.
A Deterministic Analysis of Noisy Sparse Subspace Clustering for Dimensionality-reduced Data.
Yining Wang, Yu-Xiang Wang, and Aarti Singh.
Proceedings of the 32nd International Conference on Machine Learning (ICML'15).
Differentially Private Subspace Clustering.
Yining Wang, Yu-Xiang Wang, and Aarti Singh.
Advances in Neural Information Processing Systems 28 (NIPS'15).
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo.
Yu-Xiang Wang, Stephen E. Fienberg and Alex Smola.
Proceedings of the 32nd International Conference on Machine Learning (ICML'15).
Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle.
Yu-Xiang Wang, Jing Lei, Stephen E. Fienberg.
Preprint.
Trend Filtering on Graphs.
Yu-Xiang Wang, James Sharpnack, Alex Smola, and Ryan J. Tibshirani.
Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS'15).
2014
 		Distribution-Free Prediction Bands For Non-Parametric Regression.
Jing Lei and Larry Wasserman.
Journal of the Royal Statistical Society: Series B (Statistical Methodology) 76(1).
Nonparametric Ridge Estimation.
Christopher R Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman, and others.
The Annals of Statistics 42(4).
Berry-Esseen Bounds For Estimating Undirected Graphs.
Larry Wasserman, Mladen Kolar, Alessandro Rinaldo, and others.
Electronic Journal of Statistics.
A General Framework for Fast Stagewise Algorithms.
Ryan J. Tibshirani.
Preprint.
Fast and Flexible ADMM Algorithms for Trend Filtering.
Aaditya Ramdas and Ryan J. Tibshirani.
Preprint.
Exact Post-selection Inference for Forward Stepwise and Least Angle Regression.
Jonathan Taylor, Richard Lockhart, Ryan J. Tibshirani, and Robert Tibshirani.
Preprint.
Inference in Adaptive Regression via the Kac-Rice formula.
Jonathan Taylor, Joshua Loftus, and Ryan J. Tibshirani.
Preprint.
Adaptive Piecewise Polynomial Estimation via Trend Filtering.
Ryan J. Tibshirani.
Annals of Statistics 42(1).
A Significance Test for the Lasso.
Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, and Robert Tibshirani.
Annals of Statistics 42(2).
Subspace Learning From Extremely Compressed Measurements.
Akshay Krishnamurthy, Martin Azizyan, and Aarti Singh.
Asilomar Conference on Signals, Systems and Computers.
Nonparametric Estimation of Renyi Divergence and Friends.
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabas Poczos, and Larry Wasserman.
International Conference on Machine Learning.
Enhanced Mode Clustering.
Yen-Chi Chen, Christopher R Genovese, and Larry Wasserman.
arXiv preprint journalarXiv:1406.1780.
An Analysis of Active Learning With Uniform Feature Noise.
Aaditya Ramdas, Barnabas Poczos, Aarti Singh, and Larry Wasserman.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS'14).
Margins, Kernels and Non-linear Smoothed Perceptrons.
Aaditya Ramdas and Javier Pena.
Proceedings of The 31st International Conference on Machine Learning (ICML'14).
Time Series Forecasting: Model Evaluation and Selection Using Nonparametric Risk Bounds.
Daniel J. McDonald, Cosma Rohilla Shalizi, and Mark Schervish.
Journal of Machine Learning Research in revision(1).
2013
 		Minimax theory for high-dimensional gaussian mixtures with sparse mean separation.
Martin Azizyan, Aarti Singh, and Larry Wasserman.
Advances in Neural Information Processing Systems.
Low-rank matrix and tensor completion via adaptive sampling.
Akshay Krishnamurthy and Aarti Singh.
Advances in Neural Information Processing Systems.
Recovering graph-structured activations using adaptive compressive measurements.
Akshay Krishnamurthy, James Sharpnack, and Aarti Singh.
Asilomar Conference on Signals, Systems, and Computers.
Optimal Rates For Stochastic Convex Optimization Under Tsybakov Noise Condition.
Aaditya Ramdas and Aarti Singh.
Proceedings of the 30th International Conference on Machine Learning (ICML'13).
Algorithmic connections between active learning and convex optimization.
Aaditya Ramdas and Aarti Singh.
Algorithmic Learning Theory (ALT'13).
Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction.
Georg M. Goerg and Cosma Rohilla Shalizi.
Sixteenth International Conference on Artificial Intelligence and Statistics [AIStats 2013].
Predictive PAC Learning and Process Decompositions.
Cosma Rohilla Shalizi and Aryeh (Leo) Kontorovich.
Advances in Neural Information Processing Systems 26 [NIPS 2013].
2012
 		High-Dimensional Semiparametric Gaussian Copula Graphical Models.
Han Liu, Fang Han, Ming Yuan, John Lafferty, Larry Wasserman, and others.
The Annals of Statistics 40(4).
A Comparison Of The Lasso And Marginal Regression.
Christopher R Genovese, Jiashun Jin, Larry Wasserman, and Zhigang Yao.
The Journal of Machine Learning Research 13(1).
Stability Of Density-Based Clustering.
Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, and Larry Wasserman.
The Journal of Machine Learning Research 13(1).
Minimax Manifold Estimation.
Christopher R Genovese, Marco Perone-Pacifico, Isabella Verdinelli, and Larry Wasserman.
The Journal of Machine Learning Research 13(1).
Manifold Estimation And Singular Deconvolution Under Hausdorff Loss.
Christopher R Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman, and others.
The Annals of Statistics 40(2).
The Geometry Of Nonparametric Filament Estimation.
Christopher R Genovese, Marco Perone-Pacifico, Isabella Verdinelli, and Larry Wasserman.
Journal of the American Statistical Association 107(498).
Efficient Active Algorithms For Hierarchical Clustering.
Akshay Krishnamurthy, Sivaraman Balakrishnan, Min Xu, and Aarti Singh.
International Conference on Machine Learning.
LICORS: Light Cone Reconstruction of States for Non-parametric Forecasting of Spatio-Temporal Systems.
Georg M. Goerg and Cosma Rohilla Shalizi.
Preprint.
2011
 		Noise Thresholds for Spectral Clustering.
Sivaraman Balakrishnan, Min Xu, Akshay Krishnamurthy, and Aarti Singh.
Advances in Neural Information Processing Systems.
Estimating Beta-Mixing Coefficients.
Daniel J. McDonald, Cosma Rohilla Shalizi, and Mark Schervish.
Proceedings of the $14^{\mathrm{th}}$ International Conference on Artificial Intelligence and Statistics [AISTATS 2011] (Journal of Machine Learning Research: Workshops and Conference Proceedings).
Estimating Beta-Mixing Coefficients via Histograms.
Daniel J. McDonald, Cosma Rohilla Shalizi, and Mark Schervish.
Technical Report.
Estimated VC Dimension for Risk Bounds.
Daniel J. McDonald, Cosma Rohilla Shalizi, and Mark Schervish.
Technical Report.
Risk Bounds for Autoregressive Models.
Daniel J. McDonald, Cosma Rohilla Shalizi, and Mark Schervish.
Technical Report.
Adapting to Non-stationarity with Growing Expert Ensembles.
Cosma Rohilla Shalizi, Abigail Z. Jacobs, Kristina Lisa Klinkner, and Aaron Clauset.
Technical Report.
2009
 		Dynamics of Bayesian Updating with Dependent Data and Misspecified Models.
Cosma Rohilla Shalizi.
Electronic Journal of Statistics 3(1).
2004
 		Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences.
Cosma Rohilla Shalizi and Kristina Lisa Klinkner.
Uncertainty in Artificial Intelligence: Proceedings of the Twentieth Conference (UAI 2004).
Quantifying Self-Organization with Optimal Predictors.
Cosma Rohilla Shalizi, Kristina Lisa Klinkner, and Robert Haslinger.
Physical Review Letters 93(1).

Machine Learning Department & Statistics Department at Carnegie Mellon University

Based on the UW Sampa group website