Bharath K. Sriperumbudur

According to our database1, Bharath K. Sriperumbudur authored at least 56 papers between 2007 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
On Distance and Kernel Measures of Conditional Dependence.
J. Mach. Learn. Res., 2023

Kernel ε-Greedy for Contextual Bandits.
CoRR, 2023

2022
Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA.
J. Mach. Learn. Res., 2022

Spectral Regularized Kernel Two-Sample Tests.
CoRR, 2022

Regularized Stein Variational Gradient Flow.
CoRR, 2022

Robust Topological Inference in the Presence of Outliers.
CoRR, 2022

Cycle Consistent Probability Divergences Across Different Spaces.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2020
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings.
Found. Comput. Math., 2020

Robust Persistence Diagrams using Reproducing Kernels.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Gaussian Sketching yields a J-L Lemma in RKHS.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Kernel Sketching yields Kernel JL.
CoRR, 2019

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling.
CoRR, 2019

Local minimax rates for closeness testing of discrete distributions.
CoRR, 2019

On Kernel Derivative Approximation with Random Fourier Features.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences.
CoRR, 2018

Minimax Estimation of Quadratic Fourier Functionals.
CoRR, 2018

2017
Minimax Estimation of Kernel Mean Embeddings.
J. Mach. Learn. Res., 2017

Characteristic and Universal Tensor Product Kernels.
J. Mach. Learn. Res., 2017

Density Estimation in Infinite Dimensional Exponential Families.
J. Mach. Learn. Res., 2017

Kernel Mean Embedding of Distributions: A Review and Beyond.
Found. Trends Mach. Learn., 2017

2016
Learning Theory for Distribution Regression.
J. Mach. Learn. Res., 2016

Kernel Mean Shrinkage Estimators.
J. Mach. Learn. Res., 2016

Kernel Mean Embedding of Distributions: A Review and Beyonds.
CoRR, 2016

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Convergence guarantees for kernel-based quadrature rules in misspecified settings.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Optimal Rates for Random Fourier Features.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Two-stage sampled learning theory on distributions.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding.
CoRR, 2014

Kernel Mean Estimation via Spectral Filtering.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Kernel Mean Estimation and Stein Effect.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Kernel Mean Estimation and Stein's Effect.
CoRR, 2013

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions.
Proceedings of the 30th International Conference on Machine Learning, 2013

Ultrahigh Dimensional Feature Screening via RKHS Embeddings.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory.
Neural Comput., 2012

Consistency and Rates for Clustering with DBSCAN.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Equivalence of distance-based and RKHS-based statistics in hypothesis testing
CoRR, 2012

Hypothesis testing using pairwise distances and associated kernels (with Appendix)
CoRR, 2012

Optimal kernel choice for large-scale two-sample tests.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Hypothesis testing using pairwise distances and associated kernels.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
A majorization-minimization approach to the sparse generalized eigenvalue problem.
Mach. Learn., 2011

Universality, Characteristic Kernels and RKHS Embedding of Measures.
J. Mach. Learn. Res., 2011

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Mixture density estimation via Hilbert space embedding of measures.
Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

2010
Hilbert Space Embeddings and Metrics on Probability Measures.
J. Mach. Learn. Res., 2010

On the relation between universality, characteristic kernels and RKHS embedding of measures.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Non-parametric estimation of integral probability metrics.
Proceedings of the IEEE International Symposium on Information Theory, 2010

2009
A note on integral probability metrics and $\phi$-divergences
CoRR, 2009

On the Convergence of the Concave-Convex Procedure.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

A Fast, Consistent Kernel Two-Sample Test.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Generalized Clustering via Kernel Embeddings.
Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

2008
Characteristic Kernels on Groups and Semigroups.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Metric embedding for kernel classification rules.
Proceedings of the Machine Learning, 2008

Injective Hilbert Space Embeddings of Probability Measures.
Proceedings of the 21st Annual Conference on Learning Theory, 2008

2007
Sparse eigen methods by D.C. programming.
Proceedings of the Machine Learning, 2007


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