Sparse eigen methods by D.C. programming. - Bharath K. Sriperumbudur; David A. Torres; Gert R. G. Lanckriet (ICML, 2007)

Injective Hilbert Space Embeddings of Probability Measures. - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Gert R. G. Lanckriet; Bernhard Schölkopf (COLT, 2008)

Metric embedding for kernel classification rules. - Bharath K. Sriperumbudur; Omer A. Lang; Gert R. G. Lanckriet (ICML, 2008)

Characteristic Kernels on Groups and Semigroups. - Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (NIPS, 2008)

Generalized Clustering via Kernel Embeddings. - Stefanie Jegelka; Arthur Gretton; Bernhard Schölkopf; Bharath K. Sriperumbudur; Ulrike von Luxburg (KI, 2009)

A Fast, Consistent Kernel Two-Sample Test. - Arthur Gretton; Kenji Fukumizu; Zaïd Harchaoui; Bharath K. Sriperumbudur (NIPS, 2009)

Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Gert R. G. Lanckriet; Bernhard Schölkopf (NIPS, 2009)

On the Convergence of the Concave-Convex Procedure. - Bharath K. Sriperumbudur; Gert R. G. Lanckriet (NIPS, 2009)

A note on integral probability metrics and $ phi$-divergences - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Gert R. G. Lanckriet; Bernhard Schölkopf (CoRR, 2009)

Non-parametric estimation of integral probability metrics. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf; Gert R. G. Lanckriet (ISIT, 2010)

On the relation between universality, characteristic kernels and RKHS embedding of measures. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (AISTATS, 2010)

Hilbert Space Embeddings and Metrics on Probability Measures. - Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu; Bernhard Schölkopf; Gert R. G. Lanckriet (J. Mach. Learn. Res., 2010)

Mixture density estimation via Hilbert space embedding of measures. - Bharath K. Sriperumbudur (ISIT, 2011)

Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (NIPS, 2011)

Universality, Characteristic Kernels and RKHS Embedding of Measures. - Bharath K. Sriperumbudur; Kenji Fukumizu; Gert R. G. Lanckriet (J. Mach. Learn. Res., 2011)

A majorization-minimization approach to the sparse generalized eigenvalue problem. - Bharath K. Sriperumbudur; David A. Torres; Gert R. G. Lanckriet (Mach. Learn., 2011)

Hypothesis testing using pairwise distances and associated kernels. - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (ICML, 2012)

Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Hypothesis testing using pairwise distances and associated kernels (with Appendix) - Dino Sejdinovic; Arthur Gretton; Bharath K. Sriperumbudur; Kenji Fukumizu (CoRR, 2012)

Equivalence of distance-based and RKHS-based statistics in hypothesis testing - Dino Sejdinovic; Bharath K. Sriperumbudur; Arthur Gretton; Kenji Fukumizu (CoRR, 2012)

Consistency and Rates for Clustering with DBSCAN. - Bharath K. Sriperumbudur; Ingo Steinwart (AISTATS, 2012)

A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory. - Bharath K. Sriperumbudur; Gert R. G. Lanckriet (Neural Comput., 2012)

Ultrahigh Dimensional Feature Screening via RKHS Embeddings. - Krishnakumar Balasubramanian 002; Bharath K. Sriperumbudur; Guy Lebanon (AISTATS, 2013)

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions. - Purushottam Kar; Bharath K. Sriperumbudur; Prateek Jain 002; Harish Karnick (ICML (3), 2013)

Kernel Mean Estimation and Stein's Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (CoRR, 2013)

Kernel Mean Estimation and Stein Effect. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Arthur Gretton; Bernhard Schölkopf (ICML, 2014)

Kernel Mean Estimation via Spectral Filtering. - Krikamol Muandet; Bharath K. Sriperumbudur; Bernhard Schölkopf (NIPS, 2014)

Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding. - Zoltán Szabó 001; Arthur Gretton; Barnabás Póczos; Bharath K. Sriperumbudur (CoRR, 2014)

Two-stage sampled learning theory on distributions. - Zoltán Szabó 001; Arthur Gretton; Barnabás Póczos; Bharath K. Sriperumbudur (AISTATS, 2015)

Optimal Rates for Random Fourier Features. - Bharath K. Sriperumbudur; Zoltán Szabó 001 (NIPS, 2015)

Convergence guarantees for kernel-based quadrature rules in misspecified settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (NIPS, 2016)

Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels. - Ilya O. Tolstikhin; Bharath K. Sriperumbudur; Bernhard Schölkopf (NIPS, 2016)

Kernel Mean Embedding of Distributions: A Review and Beyonds. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (CoRR, 2016)

Kernel Mean Shrinkage Estimators. - Krikamol Muandet; Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Bernhard Schölkopf (J. Mach. Learn. Res., 2016)

Learning Theory for Distribution Regression. - Zoltán Szabó 001; Bharath K. Sriperumbudur; Barnabás Póczos; Arthur Gretton (J. Mach. Learn. Res., 2016)

Kernel Mean Embedding of Distributions: A Review and Beyond. - Krikamol Muandet; Kenji Fukumizu; Bharath K. Sriperumbudur; Bernhard Schölkopf (Found. Trends Mach. Learn., 2017)

Density Estimation in Infinite Dimensional Exponential Families. - Bharath K. Sriperumbudur; Kenji Fukumizu; Arthur Gretton; Aapo Hyvärinen; Revant Kumar (J. Mach. Learn. Res., 2017)

Characteristic and Universal Tensor Product Kernels. - Zoltán Szabó 001; Bharath K. Sriperumbudur (J. Mach. Learn. Res., 2017)

Minimax Estimation of Kernel Mean Embeddings. - Ilya O. Tolstikhin; Bharath K. Sriperumbudur; Krikamol Muandet (J. Mach. Learn. Res., 2017)

Minimax Estimation of Quadratic Fourier Functionals. - Shashank Singh 005; Bharath K. Sriperumbudur; Barnabás Póczos (CoRR, 2018)

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2018)

On Kernel Derivative Approximation with Random Fourier Features. - Zoltán Szabó 001; Bharath K. Sriperumbudur (AISTATS, 2019)

Local minimax rates for closeness testing of discrete distributions. - Joseph Lam-Weil; Alexandra Carpentier; Bharath K. Sriperumbudur (CoRR, 2019)

Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (CoRR, 2019)

Kernel Sketching yields Kernel JL. - Samory Kpotufe; Bharath K. Sriperumbudur (CoRR, 2019)

Gaussian Sketching yields a J-L Lemma in RKHS. - Samory Kpotufe; Bharath K. Sriperumbudur (AISTATS, 2020)

Gain with no Pain: Efficiency of Kernel-PCA by Nyström Sampling. - Nicholas Sterge; Bharath K. Sriperumbudur; Lorenzo Rosasco; Alessandro Rudi (AISTATS, 2020)

Robust Persistence Diagrams using Reproducing Kernels. - Siddharth Vishwanath; Kenji Fukumizu; Satoshi Kuriki; Bharath K. Sriperumbudur (NeurIPS, 2020)

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings. - Motonobu Kanagawa; Bharath K. Sriperumbudur; Kenji Fukumizu (Found. Comput. Math., 2020)

Cycle Consistent Probability Divergences Across Different Spaces. - Zhengxin Zhang; Youssef Mroueh; Ziv Goldfeld; Bharath K. Sriperumbudur (AISTATS, 2022)

Robust Topological Inference in the Presence of Outliers. - Siddharth Vishwanath; Bharath K. Sriperumbudur; Kenji Fukumizu; Satoshi Kuriki (CoRR, 2022)

Spectral Regularized Kernel Two-Sample Tests. - Omar Hagrass; Bharath K. Sriperumbudur; Bing Li (CoRR, 2022)

Statistical Optimality and Computational Efficiency of Nystrom Kernel PCA. - Nicholas Sterge; Bharath K. Sriperumbudur (J. Mach. Learn. Res., 2022)

Kernel ε-Greedy for Contextual Bandits. - Sakshi Arya; Bharath K. Sriperumbudur (CoRR, 2023)

On Distance and Kernel Measures of Conditional Dependence. - Tianhong Sheng; Bharath K. Sriperumbudur (J. Mach. Learn. Res., 2023)

Adaptive Clustering Using Kernel Density Estimators. - Ingo Steinwart; Bharath K. Sriperumbudur; Philipp Thomann (J. Mach. Learn. Res., 2023)

(De)-regularized Maximum Mean Discrepancy Gradient Flow. - Zonghao Chen; Aratrika Mustafi; Pierre Glaser; Anna Korba; Arthur Gretton; Bharath K. Sriperumbudur (CoRR, 2024)

Nyström Kernel Stein Discrepancy. - Florian Kalinke; Zoltán Szabó 001; Bharath K. Sriperumbudur (AISTATS, 2025)

Uniform Kernel Prober. - Soumya Mukherjee; Bharath K. Sriperumbudur (CoRR, 2025)

Minimax Optimal Kernel Two-Sample Tests with Random Features. - Soumya Mukherjee; Bharath K. Sriperumbudur (CoRR, 2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2025)

Regularized Stein Variational Gradient Flow. - Ye He 003; Krishnakumar Balasubramanian 002; Bharath K. Sriperumbudur; Jianfeng Lu (Found. Comput. Math., 2025)