Larry A. Wasserman

ORCID

Publication Activity (10 Years)

Years Active: 1992-2023
Publications (10 Years): 22

Top Venues

Publications

Jin-Hong Du, Larry A. Wasserman, Kathryn Roeder
Simultaneous inference for generalized linear models with unmeasured confounders. CoRR (2023)
Boyan Duan, Aaditya Ramdas, Larry A. Wasserman
Interactive rank testing by betting. CLeaR (2022)
Isabella Verdinelli, Larry A. Wasserman
Forest Guided Smoothing. CoRR (2021)
Robin Dunn, Larry A. Wasserman, Aaditya Ramdas
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity. CoRR (2021)
Kwangho Kim, Jisu Kim, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman
Efficient Topological Layer based on Persistent Landscapes. CoRR (2020)
Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry A. Wasserman
The huge Package for High-dimensional Undirected Graph Estimation in R. CoRR (2020)
Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman
PLLay: Efficient Topological Layer based on Persistent Landscapes. NeurIPS (2020)
Jisu Kim, Jaehyeok Shin, Frédéric Chazal, Alessandro Rinaldo, Larry A. Wasserman
Homotopy Reconstruction via the Cech Complex and the Vietoris-Rips Complex. SoCG (2020)
Boyan Duan, Aaditya Ramdas, Larry A. Wasserman
Familywise Error Rate Control by Interactive Unmasking. ICML (2020)
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension. ICML (2019)
Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman
Nerve Theorem on a Positive Reach set. CoRR (2019)
Jisu Kim, Alessandro Rinaldo, Larry A. Wasserman
Minimax rates for estimating the dimension of a manifold. J. Comput. Geom. 10 (1) (2019)
Yotam Hechtlinger, Barnabás Póczos, Larry A. Wasserman
Cautious Deep Learning. CoRR (2018)
Frédéric Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman
Robust Topological Inference: Distance To a Measure and Kernel Distance. J. Mach. Learn. Res. 18 (2017)
Sivaraman Balakrishnan, Larry A. Wasserman
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. CoRR (2017)
Sivaraman Balakrishnan, Larry A. Wasserman
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. CoRR (2017)
Jisu Kim, Alessandro Rinaldo, Larry A. Wasserman
Minimax Rates for Estimating the Dimension of a Manifold. CoRR (2016)
Mauricio Sadinle, Jing Lei, Larry A. Wasserman
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels. CoRR (2016)
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman
Statistical Inference for Cluster Trees. NIPS (2016)
Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman
Minimax Lower Bounds for Linear Independence Testing. CoRR (2016)
Aaditya Ramdas, Aarti Singh, Larry A. Wasserman
Classification Accuracy as a Proxy for Two Sample Testing. CoRR (2016)
Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman
Minimax lower bounds for linear independence testing. ISIT (2016)
Aaditya Ramdas, Sashank Jakkam Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions. AAAI (2015)
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
On Estimating L22 Divergence. AISTATS (2015)
Frédéric Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman
Subsampling Methods for Persistent Homology. ICML (2015)
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS (2015)
Martin Azizyan, Yen-Chi Chen, Aarti Singh, Larry A. Wasserman
Risk Bounds For Mode Clustering. CoRR (2015)
Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
An Analysis of Active Learning With Uniform Feature Noise. CoRR (2015)
Yiye Zhang, Rema Padman, Larry A. Wasserman, Nirav Patel, Pradip Teredesai, Qizhi Xie
On Clinical Pathway Discovery from Electronic Health Record Data. IEEE Intell. Syst. 30 (1) (2015)
Mutian Fu, Guangyu Xia, Roger B. Dannenberg, Larry A. Wasserman
A Statistical View on the Expressive Timing of Piano Rolled Chords. ISMIR (2015)
Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing. CoRR (2015)
Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman
Optimal Ridge Detection using Coverage Risk. NIPS (2015)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman
Stochastic convergence of persistence landscapes and silhouettes. J. Comput. Geom. 6 (2) (2015)
Jing Lei, Alessandro Rinaldo, Larry A. Wasserman
A conformal prediction approach to explore functional data. Ann. Math. Artif. Intell. 74 (1-2) (2015)
Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives. AISTATS (2015)
Martin Azizyan, Aarti Singh, Larry A. Wasserman
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures. AISTATS (2015)
Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman
Statistical analysis of metric graph reconstruction. J. Mach. Learn. Res. 15 (1) (2014)
Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR (2014)
Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins
Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations. CoRR (2014)
Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
An Analysis of Active Learning with Uniform Feature Noise. AISTATS (2014)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman
Stochastic Convergence of Persistence Landscapes and Silhouettes. SoCG (2014)
Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman
Nonparametric Estimation of Renyi Divergence and Friends. ICML (2014)
Yen-Chi Chen, Christopher R. Genovese, Larry A. Wasserman
Generalized Mode and Ridge Estimation. CoRR (2014)
Yiye Zhang, Rema Padman, Larry A. Wasserman
On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease. AMIA (2014)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman
Subsampling Methods for Persistent Homology. CoRR (2014)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman
Robust Topological Inference: Distance To a Measure and Kernel Distance. CoRR (2014)
Aaditya Ramdas, Sashank J. Reddi, Barnabás Póczos, Aarti Singh, Larry A. Wasserman
On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives. CoRR (2014)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
On the Bootstrap for Persistence Diagrams and Landscapes. CoRR (2013)
Jing Lei, Alessandro Rinaldo, Larry A. Wasserman
A Conformal Prediction Approach to Explore Functional Data CoRR (2013)
Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman
Distribution-Free Distribution Regression. AISTATS (2013)
Yen-Chi Chen, Christopher R. Genovese, Larry A. Wasserman
Uncertainty Measures and Limiting Distributions for Filament Estimation. CoRR (2013)
Rob Hall, Alessandro Rinaldo, Larry A. Wasserman
Differential privacy for functions and functional data. J. Mach. Learn. Res. 14 (1) (2013)
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Cluster Trees on Manifolds. NIPS (2013)
Larry A. Wasserman, Mladen Kolar, Alessandro Rinaldo
Estimating Undirected Graphs Under Weak Assumptions. CoRR (2013)
Martin Azizyan, Aarti Singh, Larry A. Wasserman
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation. NIPS (2013)
Sivaraman Balakrishnan, Brittany Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Statistical Inference For Persistent Homology CoRR (2013)
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman
Nonparametric Inference For Density Modes. CoRR (2013)
Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Cluster Trees on Manifolds. CoRR (2013)
Martin Azizyan, Aarti Singh, Larry A. Wasserman
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation. CoRR (2013)
Robert J. Hall, Larry A. Wasserman, Alessandro Rinaldo
Random Differential Privacy. J. Priv. Confidentiality 4 (2) (2013)
John D. Lafferty, Larry A. Wasserman
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk CoRR (2013)
Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Tight Lower Bounds for Homology Inference. CoRR (2013)
Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman
Stochastic Convergence of Persistence Landscapes and Silhouettes. CoRR (2013)
Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman
Statistical Analysis of Metric Graph Reconstruction CoRR (2013)
Barnabás Póczos, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman
Distribution-Free Distribution Regression CoRR (2013)
Rob Hall, Alessandro Rinaldo, Larry A. Wasserman
Differential Privacy for Functions and Functional Data CoRR (2012)
Han Liu, John D. Lafferty, Larry A. Wasserman
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS (2012)
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
Minimax rates for homology inference. AISTATS (2012)
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman
Minimax Manifold Estimation. J. Mach. Learn. Res. 13 (2012)
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman
Nonparametric Ridge Estimation CoRR (2012)
Larry A. Wasserman
Minimaxity, Statistical Thinking and Differential Privacy. J. Priv. Confidentiality 4 (1) (2012)
Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML (2012)
Jing Lei, Larry A. Wasserman
Distribution Free Prediction Bands CoRR (2012)
Tuo Zhao, Han Liu, Kathryn Roeder, John D. Lafferty, Larry A. Wasserman
The huge Package for High-dimensional Undirected Graph Estimation in R. J. Mach. Learn. Res. 13 (2012)
Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry A. Wasserman
Stability of density-based clustering. J. Mach. Learn. Res. 13 (2012)
Christopher R. Genovese, Jiashun Jin, Larry A. Wasserman, Zhigang Yao
A Comparison of the Lasso and Marginal Regression. J. Mach. Learn. Res. 13 (2012)
John D. Lafferty, Han Liu, Larry A. Wasserman
Sparse Nonparametric Graphical Models CoRR (2012)
Martin Azizyan, Aarti Singh, Larry A. Wasserman
Density-Sensitive Semisupervised Inference CoRR (2012)
Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman
The Nonparanormal SKEPTIC. CoRR (2012)
Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman
Forest Density Estimation. J. Mach. Learn. Res. 12 (2011)
Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman
Minimax Rates for Homology Inference CoRR (2011)
Rob Hall, Alessandro Rinaldo, Larry A. Wasserman
Random Differential Privacy CoRR (2011)
Jing Lei, James M. Robins, Larry A. Wasserman
Efficient Nonparametric Conformal Prediction Regions CoRR (2011)
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman
Manifold Estimation and Singular Deconvolution Under Hausdorff Loss CoRR (2011)
Mladen Kolar, John D. Lafferty, Larry A. Wasserman
Union Support Recovery in Multi-task Learning. J. Mach. Learn. Res. 12 (2011)
Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman
Graph-Valued Regression. NIPS (2010)
Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman
Minimax Manifold Estimation CoRR (2010)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Time varying undirected graphs. Mach. Learn. 80 (2-3) (2010)
Han Liu, Kathryn Roeder, Larry A. Wasserman
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. NIPS (2010)
Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu
Forest Density Estimation. COLT (2010)
Han Liu, John D. Lafferty, Larry A. Wasserman
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. J. Mach. Learn. Res. 10 (2009)
Roger B. Dannenberg, Larry A. Wasserman
Estimating the Error Distribution of a Single Tap Sequence without Ground Truth. ISMIR (2009)
Shuheng Zhou, Katrina Ligett, Larry A. Wasserman
Differential privacy with compression. ISIT (2009)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed and Privacy-Sensitive Sparse Regression. IEEE Trans. Inf. Theory 55 (2) (2009)
Han Liu, John D. Lafferty, Larry A. Wasserman
Nonparametric regression and classification with joint sparsity constraints. NIPS (2008)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Time Varying Undirected Graphs. COLT (2008)
Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman
SpAM: Sparse Additive Models. NIPS (2007)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed Regression CoRR (2007)
John D. Lafferty, Larry A. Wasserman
Statistical Analysis of Semi-Supervised Regression. NIPS (2007)
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
Compressed Regression. NIPS (2007)

Larry A. Wasserman.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Larry A. Wasserman