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Larry A. Wasserman
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- affiliation: Carnegie Mellon University, Pittsburgh, USA
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2020 – today
- 2024
- [j21]Isabella Verdinelli, Larry A. Wasserman:
Decorrelated Variable Importance. J. Mach. Learn. Res. 25: 7:1-7:27 (2024) - [i48]Jin-Hong Du, Kathryn Roeder, Larry A. Wasserman:
Assumption-Lean Post-Integrated Inference with Negative Control Outcomes. CoRR abs/2410.04996 (2024) - 2023
- [i47]Jin-Hong Du
, Larry A. Wasserman, Kathryn Roeder:
Simultaneous inference for generalized linear models with unmeasured confounders. CoRR abs/2309.07261 (2023) - 2022
- [c41]Boyan Duan, Aaditya Ramdas, Larry A. Wasserman:
Interactive rank testing by betting. CLeaR 2022: 201-235 - 2021
- [i46]Isabella Verdinelli, Larry A. Wasserman:
Forest Guided Smoothing. CoRR abs/2103.05092 (2021) - [i45]Robin Dunn, Larry A. Wasserman, Aaditya Ramdas:
Universal Inference Meets Random Projections: A Scalable Test for Log-concavity. CoRR abs/2111.09254 (2021) - 2020
- [c40]Jisu Kim, Jaehyeok Shin, Frédéric Chazal, Alessandro Rinaldo, Larry A. Wasserman:
Homotopy Reconstruction via the Cech Complex and the Vietoris-Rips Complex. SoCG 2020: 54:1-54:19 - [c39]Boyan Duan, Aaditya Ramdas, Larry A. Wasserman:
Familywise Error Rate Control by Interactive Unmasking. ICML 2020: 2720-2729 - [c38]Kwangho Kim, Jisu Kim, Manzil Zaheer, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman:
PLLay: Efficient Topological Layer based on Persistent Landscapes. NeurIPS 2020 - [i44]Kwangho Kim, Jisu Kim, Joon Sik Kim, Frédéric Chazal, Larry A. Wasserman:
Efficient Topological Layer based on Persistent Landscapes. CoRR abs/2002.02778 (2020) - [i43]Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry A. Wasserman:
The huge Package for High-dimensional Undirected Graph Estimation in R. CoRR abs/2006.14781 (2020)
2010 – 2019
- 2019
- [j20]Jisu Kim, Alessandro Rinaldo, Larry A. Wasserman:
Minimax rates for estimating the dimension of a manifold. J. Comput. Geom. 10(1): 42-95 (2019) - [c37]Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman:
Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension. ICML 2019: 3398-3407 - [i42]Jisu Kim, Jaehyeok Shin, Alessandro Rinaldo, Larry A. Wasserman:
Nerve Theorem on a Positive Reach set. CoRR abs/1903.06955 (2019) - 2018
- [i41]Yotam Hechtlinger, Barnabás Póczos, Larry A. Wasserman:
Cautious Deep Learning. CoRR abs/1805.09460 (2018) - 2017
- [j19]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: 159:1-159:40 (2017) - [i40]Sivaraman Balakrishnan, Larry A. Wasserman:
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates. CoRR abs/1706.10003 (2017) - [i39]Sivaraman Balakrishnan, Larry A. Wasserman:
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review. CoRR abs/1712.06120 (2017) - 2016
- [c36]Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman:
Minimax lower bounds for linear independence testing. ISIT 2016: 965-969 - [c35]Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry A. Wasserman:
Statistical Inference for Cluster Trees. NIPS 2016: 1831-1839 - [i38]Aaditya Ramdas, David Isenberg, Aarti Singh, Larry A. Wasserman:
Minimax Lower Bounds for Linear Independence Testing. CoRR abs/1601.06259 (2016) - [i37]Aaditya Ramdas, Aarti Singh, Larry A. Wasserman:
Classification Accuracy as a Proxy for Two Sample Testing. CoRR abs/1602.02210 (2016) - [i36]Jisu Kim, Alessandro Rinaldo, Larry A. Wasserman:
Minimax Rates for Estimating the Dimension of a Manifold. CoRR abs/1605.01011 (2016) - [i35]Mauricio Sadinle, Jing Lei, Larry A. Wasserman:
Least Ambiguous Set-Valued Classifiers with Bounded Error Levels. CoRR abs/1609.00451 (2016) - 2015
- [j18]Jing Lei, Alessandro Rinaldo, Larry A. Wasserman:
A conformal prediction approach to explore functional data. Ann. Math. Artif. Intell. 74(1-2): 29-43 (2015) - [j17]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): 70-75 (2015) - [j16]Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman:
Stochastic convergence of persistence landscapes and silhouettes. J. Comput. Geom. 6(2): 140-161 (2015) - [c34]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: 3571-3577 - [c33]Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures. AISTATS 2015 - [c32]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
On Estimating L22 Divergence. AISTATS 2015 - [c31]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 - [c30]Frédéric Chazal, Brittany Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman:
Subsampling Methods for Persistent Homology. ICML 2015: 2143-2151 - [c29]Mutian Fu, Guangyu Xia, Roger B. Dannenberg, Larry A. Wasserman:
A Statistical View on the Expressive Timing of Piano Rolled Chords. ISMIR 2015: 578-583 - [c28]Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry A. Wasserman:
Optimal Ridge Detection using Coverage Risk. NIPS 2015: 316-324 - [c27]Kirthevasan Kandasamy, Akshay Krishnamurthy, Barnabás Póczos, Larry A. Wasserman, James M. Robins:
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations. NIPS 2015: 397-405 - [i34]Martin Azizyan, Yen-Chi Chen, Aarti Singh, Larry A. Wasserman:
Risk Bounds For Mode Clustering. CoRR abs/1505.00482 (2015) - [i33]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning With Uniform Feature Noise. CoRR abs/1505.04215 (2015) - [i32]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 abs/1508.00655 (2015) - 2014
- [j15]Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman:
Statistical analysis of metric graph reconstruction. J. Mach. Learn. Res. 15(1): 3425-3446 (2014) - [c26]Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
An Analysis of Active Learning with Uniform Feature Noise. AISTATS 2014: 805-813 - [c25]Yiye Zhang, Rema Padman, Larry A. Wasserman:
On Learning and Visualizing Practice-based Clinical Pathways for Chronic Kidney Disease. AMIA 2014 - [c24]Frédéric Chazal, Brittany Terese Fasy
Stochastic Convergence of Persistence Landscapes and Silhouettes. SoCG 2014: 474 - [c23]Akshay Krishnamurthy, Kirthevasan Kandasamy, Barnabás Póczos, Larry A. Wasserman:
Nonparametric Estimation of Renyi Divergence and Friends. ICML 2014: 919-927 - [i31]Yen-Chi Chen, Christopher R. Genovese, Larry A. Wasserman:
Generalized Mode and Ridge Estimation. CoRR abs/1406.1803 (2014) - [i30]Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Bertrand Michel, Alessandro Rinaldo, Larry A. Wasserman:
Subsampling Methods for Persistent Homology. CoRR abs/1406.1901 (2014) - [i29]Sashank J. Reddi, Aaditya Ramdas, Barnabás Póczos, Aarti Singh, Larry A. Wasserman:
Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions. CoRR abs/1406.2083 (2014) - [i28]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 abs/1411.4342 (2014) - [i27]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 abs/1411.6314 (2014) - [i26]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 abs/1412.7197 (2014) - 2013
- [j14]Rob Hall, Alessandro Rinaldo, Larry A. Wasserman:
Differential privacy for functions and functional data. J. Mach. Learn. Res. 14(1): 703-727 (2013) - [j13]Robert J. Hall, Larry A. Wasserman
Random Differential Privacy. J. Priv. Confidentiality 4(2) (2013) - [c22]Barnabás Póczos, Aarti Singh, Alessandro Rinaldo, Larry A. Wasserman:
Distribution-Free Distribution Regression. AISTATS 2013: 507-515 - [c21]Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation. NIPS 2013: 2139-2147 - [c20]Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Cluster Trees on Manifolds. NIPS 2013: 2679-2687 - [i25]John D. Lafferty, Larry A. Wasserman:
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. CoRR abs/1301.2286 (2013) - [i24]Barnabás Póczos, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Distribution-Free Distribution Regression. CoRR abs/1302.0082 (2013) - [i23]Jing Lei, Alessandro Rinaldo, Larry A. Wasserman:
A Conformal Prediction Approach to Explore Functional Data. CoRR abs/1302.6452 (2013) - [i22]Sivaraman Balakrishnan, Brittany Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Statistical Inference For Persistent Homology. CoRR abs/1303.7117 (2013) - [i21]Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman:
Statistical Analysis of Metric Graph Reconstruction. CoRR abs/1305.1212 (2013) - [i20]Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation. CoRR abs/1306.2035 (2013) - [i19]Sivaraman Balakrishnan, Srivatsan Narayanan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Cluster Trees on Manifolds. CoRR abs/1307.6515 (2013) - [i18]Sivaraman Balakrishnan, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
Tight Lower Bounds for Homology Inference. CoRR abs/1307.7666 (2013) - [i17]Larry A. Wasserman, Mladen Kolar, Alessandro Rinaldo:
Estimating Undirected Graphs Under Weak Assumptions. CoRR abs/1309.6933 (2013) - [i16]Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Aarti Singh, Larry A. Wasserman:
On the Bootstrap for Persistence Diagrams and Landscapes. CoRR abs/1311.0376 (2013) - [i15]Frédéric Chazal, Brittany Terese Fasy, Fabrizio Lecci, Alessandro Rinaldo, Larry A. Wasserman:
Stochastic Convergence of Persistence Landscapes and Silhouettes. CoRR abs/1312.0308 (2013) - [i14]Yen-Chi Chen, Christopher R. Genovese, Larry A. Wasserman:
Uncertainty Measures and Limiting Distributions for Filament Estimation. CoRR abs/1312.2098 (2013) - [i13]Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Nonparametric Inference For Density Modes. CoRR abs/1312.7567 (2013) - 2012
- [j12]Alessandro Rinaldo, Aarti Singh, Rebecca Nugent, Larry A. Wasserman:
Stability of density-based clustering. J. Mach. Learn. Res. 13: 905-948 (2012) - [j11]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: 1059-1062 (2012) - [j10]Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Minimax Manifold Estimation. J. Mach. Learn. Res. 13: 1263-1291 (2012) - [j9]Christopher R. Genovese, Jiashun Jin, Larry A. Wasserman, Zhigang Yao:
A Comparison of the Lasso and Marginal Regression. J. Mach. Learn. Res. 13: 2107-2143 (2012) - [j8]Larry A. Wasserman
Minimaxity, Statistical Thinking and Differential Privacy. J. Priv. Confidentiality 4(1) (2012) - [c19]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
High Dimensional Semiparametric Gaussian Copula Graphical Models. ICML 2012 - [c18]Han Liu, John D. Lafferty, Larry A. Wasserman:
Exponential Concentration for Mutual Information Estimation with Application to Forests. NIPS 2012: 2546-2554 - [c17]Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman:
Minimax rates for homology inference. AISTATS 2012: 64-72 - [i12]John D. Lafferty, Han Liu, Larry A. Wasserman:
Sparse Nonparametric Graphical Models. CoRR abs/1201.0794 (2012) - [i11]Rob Hall, Alessandro Rinaldo, Larry A. Wasserman:
Differential Privacy for Functions and Functional Data. CoRR abs/1203.2570 (2012) - [i10]Jing Lei, Larry A. Wasserman:
Distribution Free Prediction Bands. CoRR abs/1203.5422 (2012) - [i9]Martin Azizyan, Aarti Singh, Larry A. Wasserman:
Density-Sensitive Semisupervised Inference. CoRR abs/1204.1685 (2012) - [i8]Han Liu, Fang Han, Ming Yuan, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal SKEPTIC. CoRR abs/1206.6488 (2012) - [i7]Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Nonparametric Ridge Estimation. CoRR abs/1212.5156 (2012) - 2011
- [j7]Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman:
Forest Density Estimation. J. Mach. Learn. Res. 12: 907-951 (2011) - [j6]Mladen Kolar, John D. Lafferty, Larry A. Wasserman:
Union Support Recovery in Multi-task Learning. J. Mach. Learn. Res. 12: 2415-2435 (2011) - [i6]Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Manifold Estimation and Singular Deconvolution Under Hausdorff Loss. CoRR abs/1109.4540 (2011) - [i5]Jing Lei, James M. Robins, Larry A. Wasserman:
Efficient Nonparametric Conformal Prediction Regions. CoRR abs/1111.1418 (2011) - [i4]Rob Hall, Alessandro Rinaldo, Larry A. Wasserman:
Random Differential Privacy. CoRR abs/1112.2680 (2011) - [i3]Sivaraman Balakrishnan, Alessandro Rinaldo, Don Sheehy, Aarti Singh, Larry A. Wasserman:
Minimax Rates for Homology Inference. CoRR abs/1112.5627 (2011) - 2010
- [j5]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Time varying undirected graphs. Mach. Learn. 80(2-3): 295-319 (2010) - [c16]Anupam Gupta, John D. Lafferty, Han Liu, Larry A. Wasserman, Min Xu:
Forest Density Estimation. COLT 2010: 394-406 - [c15]Han Liu, Xi Chen, John D. Lafferty, Larry A. Wasserman:
Graph-Valued Regression. NIPS 2010: 1423-1431 - [c14]Han Liu, Kathryn Roeder, Larry A. Wasserman:
Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. NIPS 2010: 1432-1440 - [i2]Christopher R. Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry A. Wasserman:
Minimax Manifold Estimation. CoRR abs/1007.0549 (2010)
2000 – 2009
- 2009
- [j4]Han Liu, John D. Lafferty, Larry A. Wasserman:
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs. J. Mach. Learn. Res. 10: 2295-2328 (2009) - [j3]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed and Privacy-Sensitive Sparse Regression. IEEE Trans. Inf. Theory 55(2): 846-866 (2009) - [c13]Shuheng Zhou, Katrina Ligett
Differential privacy with compression. ISIT 2009: 2718-2722 - [c12]Roger B. Dannenberg, Larry A. Wasserman:
Estimating the Error Distribution of a Single Tap Sequence without Ground Truth. ISMIR 2009: 297-302 - 2008
- [c11]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Time Varying Undirected Graphs. COLT 2008: 455-466 - [c10]Han Liu, John D. Lafferty, Larry A. Wasserman:
Nonparametric regression and classification with joint sparsity constraints. NIPS 2008: 969-976 - 2007
- [c9]John D. Lafferty, Larry A. Wasserman:
Statistical Analysis of Semi-Supervised Regression. NIPS 2007: 801-808 - [c8]Pradeep Ravikumar, Han Liu, John D. Lafferty, Larry A. Wasserman:
SpAM: Sparse Additive Models. NIPS 2007: 1201-1208 - [c7]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression. NIPS 2007: 1713-1720 - [c6]Han Liu, John D. Lafferty, Larry A. Wasserman:
Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo. AISTATS 2007: 283-290 - [i1]Shuheng Zhou, John D. Lafferty, Larry A. Wasserman:
Compressed Regression. CoRR abs/0706.0534 (2007) - 2005
- [c5]Brent Bryan, Jeff G. Schneider, Robert Nichol, Christopher J. Miller, Christopher R. Genovese, Larry A. Wasserman:
Active Learning For Identifying Function Threshold Boundaries. NIPS 2005: 163-170 - [c4]John D. Lafferty, Larry A. Wasserman:
Rodeo: Sparse Nonparametric Regression in High Dimensions. NIPS 2005: 707-714 - 2001
- [c3]John D. Lafferty, Larry A. Wasserman:
Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk. UAI 2001: 293-300
1990 – 1999
- 1999
- [j2]Sebastian Thrun, Christos Faloutsos, Tom M. Mitchell, Larry A. Wasserman:
Automated Learning and Discovery State-of-the-Art and Research Topics in a Rapidly Growing Field. AI Mag. 20(3): 78-82 (1999) - [c2]Larry A. Wasserman, Alan N. Willson Jr.:
A variable-rate filtering system for digital communications. ICASSP 1999: 1497-1500 - 1997
- [c1]James M. Robins, Larry A. Wasserman:
Estimation of Effects of Sequential Treatments by Reparameterizing Directed Acyclic Graphs. UAI 1997: 409-420 - 1992
- [j1]Larry A. Wasserman:
Comments on shafer's "perspectives on the theory and practice of belief functions". Int. J. Approx. Reason. 6(3): 367-375 (1992)
Coauthor Index
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