Bharath K Sriperumbudur

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Preprints

Kernel single-index bandits: Estimation, inference and learning
S. Arya and S. Bhattacharjee and B. K. Sriperumbudur
[arxiv]

Minimax optimal estimation of mean and covariance functions with spectral regularization
N. Gupta and B. K. Sriperumbudur
[arxiv]

Minimax optimal kernel two-sample tests with random features
S. Mukherjee and B. K. Sriperumbudur
[arxiv]

Gaussian processes and reproducing kernels: Connections and equivalences
M. Kanagawa, P. Hennig, D. Sejdinovic, and B. K. Sriperumbudur
[arxiv]

Uniform kernel prober
S. Mukherjee and B. K. Sriperumbudur
[arxiv]

Kernel epsilon-greedy for contextual bandits
S. Arya and B. K. Sriperumbudur
[arxiv]

Robust topological inference in the presence of outliers
S. Vishwanath, B. K. Sriperumbudur, K. Fukumizu and S. Kuriki
[arxiv]

Mean shrinkage estimation for high-dimensional diagonal natural exponential families
N. Siapoutis, D. Richards and B. K. Sriperumbudur
[arxiv]

Minimax estimation of quadratic Fourier functionals
S. Singh, B. K. Sriperumbudur and B. Poczos
[arxiv]

 Gaussian processes and kernel methods: A review on connections and equivalences
M. Kanagawa, P. Hennig, D. Sejdinovic and B. K. Sriperumbudur
[arxiv]


2026

Minimax optimal goodness-of-fit tests with kernel Stein discrepancy
O. Hagrass, B. K. Sriperumbudur, and K. Balasubramanian
Bernoulli, 32(1): 299-324, 2026. [arxiv]


2025

(De)-regularized maximum mean discrepancy gradient flow
Z. Chen, A. Mustafi, P. Glaser, A. Korba, A. Gretton, and B. K. Sriperumbudur
Journal of Machine Learning Research, 26 (235): 1-77, 2025.  [arxiv] [pdf]

Nystrom kernel Stein discrepancy
 F. Kalinke, Z. Szabo, and B. K. Sriperumbudur
International Conference on Artificial Intelligence and Statistics, 2025. [arxiv]

Gradient flows and Riemannian structure in the Gromov-Wasserstein geometry
Z. Zhang, Z. Goldfeld, K. Greenewald, Y. Mroueh, and B. K. Sriperumbudur
Foundations of Computational Mathematics, 2025. [arxiv]

Functional linear and single-index models: A unified approach via Gaussian Stein identity
K. Balasubramanian, H-G. Muller, and B. K. Sriperumbudur
 Bernoulli, 31(2): 973-1006, 2025. [arxiv]

Optimal rates for functional linear regression with general regularization
N. Gupta, S. Sivananthan, and B. K. Sriperumbudur
Applied and Computational Harmonic Analysis, 76: 101745, 2025. [arxiv]

2024

On the limits of topological data analysis for statistical inference
S. Vishwanath, K. Fukumizu, S. Kuriki and B. K. Sriperumbudur
Foundations of Data Science, 2024. [arxiv] [pdf]

Regularized Stein variational gradient flow
 Y. He, K. Balasubramanian, B. K. Sriperumbudur, and J. Lu
Foundations of Computational Mathematics, 2024. [arxiv] [pdf]

Spectral regularized kernel goodness-of-fit tests
O. Hagrass, B. K. Sriperumbudur, and B. Li
Journal of Machine Learning Research, 25(309): 1-52, 2024. [pdf]

Gromov-Wasserstein distances: Entropic regularization, duality and sample complexity
Z. Zhang, Z. Goldfeld, Y. Mroueh, and B. K. Sriperumbudur
Annals of Statistics, 52(4): 1616-1645, 2024. [arxiv]

Spectral regularized kernel two-sample tests
O. Hagrass, B. K. Sriperumbudur, and B. Li
Annals of Statistics, 52(3): 1076-1101, 2024. [arxiv]

Shrinkage estimation of higher-order Bochner integrals
S. Utpala, and B. K. Sriperumbudur
Bernoulli, 30(4): 2721-2746, 2024. [arxiv]


2023

Convergence analysis of kernel conjugate gradient for functional linear regression
N. Gupta, S. Sivananthan, and B. K. Sriperumbudur
Journal of Applied and Numerical Analysis, 1: 33-47, 2023. [arxiv]

Adaptive clustering using kernel density estimators
 I. Steinwart, B. K. Sriperumbudur and P. Thomann
Journal of Machine Learning Research, 24(275): 1-56, 2023. [pdf]

On distance and kernel measures of conditional dependence
 T. Sheng and B. K. Sriperumbudur
Journal of Machine Learning Research, 24(7): 1-16, 2023. [pdf]

Optimal function-on-scalar regression over complex domains
 M. Reimherr, B. K. Sriperumbudur and Hyun Bin Kang
Electronic Journal of Statistics, 17(1): 156-197, 2023. [pdf]


2022

Statistical optimality and computational efficiency of Nystrom kernel PCA
N. Sterge and B. K. Sriperumbudur
Journal of Machine Learning Research, 23(337): 1-32, 2022. [pdf]

Approximate kernel PCA using random features: Computational vs. statistical trade-off
B. K. Sriperumbudur and N. Sterge
Annals of Statistics, 50(5): 2713-2736, 2022. [arxiv]

Cycle consistent probability divergences across different spaces
Z. Zhang, Y. Mroueh, Z. Goldfeld and B. K. Sriperumbudur
International Conference on Artificial Intelligence and Statistics, 2022. [arxiv]

Local minimax rates for closeness testing of discrete distributions
J. Lam-Weil, A. Carpentier and B. K. Sriperumbudur
Bernoulli, 28(2): 1179-1197, 2022. [arxiv]


2020

Robust persistence diagrams using reproducing kernels
S. Viswanath, K. Fukumzu, S. Kuriki and B. K. Sriperumbudur
Neural Information Processing Systems, 2020. [arxiv]

Gaussian sketching yields a J-L lemma in RKHS
S. Kpotufe and B. K. Sriperumbudur
International Conference on Artificial Intelligence and Statistics, 2020. [arxiv]

Gain with no pain: Efficient kernel-PCA by Nystrom sampling
 N. Sterge, B. K. Sriperumbudur, L. Rosasco and A. Rudi
International Conference on Artificial Intelligence and Statistics, 2020. [arxiv]

Convergence analysis of deterministic kernel-based quadrature rules in misspecified settings
M. Kanagawa, B. K. Sriperumbudur and K. Fukumizu
Foundations of Computational Mathematics, 20, 155-194, 2020. [pdf]


2019

On kernel derivative approximation with random Fourier features
Z. Szabo and B. K. Sriperumbudur
International Conference on Artificial Intelligence and Statistics, 2019. [arxiv]


2018

Optimal prediction for additive function-on-function regression
M. Reimherr, B. K. Sriperumbudur and B. Taoufik
Electronic Journal of Statistics, 12(2), 4571-4601, 2018. [pdf]

Characteristic and universal tensor product kernels
Z. Szabo and B. K. Sriperumbudur
Journal of Machine Learning Research, 18(233): 1-29, 2018. [pdf]


2017

Minimax estimation of kernel mean embeddings
I. Tolstikhin, B. K. Sriperumbudur, and K. Muandet
Journal of Machine Learning Research, 18(86): 1-47, 2017. [pdf]

Density estimation in infinite dimensional exponential families
 B. K. Sriperumbudur, K. Fukumizu, A. Gretton, A. Hyvarinen and R. Kumar
Journal of Machine Learning Research, 18(57):1-59, 2017. [pdf]

Kernel mean embedding of distributions: A review and beyond
 K. Muandet, K. Fukumizu, B. K. Sriperumbudur and B. Scholkopf
Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017. [arxiv]


2016

Convergence guarantees for kernel-based quadrature rules in misspecified settings
M. Kanagawa. B. K. Sriperumbudur and K. Fukumizu
Neural Information Processing Systems, 2016. [pdf]

Minimax estimation of maximal mean discrepancy with radial kernels
I. Tolstikhin, B. K. Sriperumbudur and B. Scholkopf
Neural Information Processing Systems, 2016. [pdf]

Learning theory for distribution regression
Z. Szabo, B. K. Sriperumbudur, B. Poczos and A. Gretton
Journal of Machine Learning Research, 17 (152):1-40, 2016. [pdf]

Kernel mean shrinkage estimators
K. Muandet, B. K. Sriperumbudur, K. Fukumizu, A. Gretton and B. Scholkopf
 Journal of Machine Learning Research, 17 (48):1-41, 2016. [pdf]

On the optimal estimation of probability measures in weak and strong topologies
B. K. Sriperumbudur
Bernoulli, 22(3): 1839-1893, 2016. [arxiv]


2015

Optimal rates for random Fourier features
B. K. Sriperumbudur and Z. Szabo
Neural Information Processing Systems, 2015. [pdf]

Two-stage sampled learning theory on distributions
Z. Szabo, A. Gretton, B. Poczos and B. K. Sriperumbudur
International Conference on Artificial Intelligence and Statistics, 2015. [pdf]


2014

Kernel mean estimation via spectral filtering
K. Muandet, B. K. Sriperumbudur and B. Scholkopf
Neural Information Processing Systems, 2014. [pdf,supplement]

Kernel mean estimation and Stein's effect
K. Muandet, K. Fukumizu, B. K. Sriperumbudur, A. Gretton and B. Scholkopf
International Conference of Machine Learning, 2014. [pdf,supplement]


2013

Equivalence of distance-based and RKHS-based statistics in hypothesis testing
D. Sejdinovic, B. K. Sriperumbudur, A. Gretton and K. Fukumizu
Annals of Statistics, 41(5): 2263-2291, 2013. [pdf]
 
On the generalization ability of online learning algorithms for pairwise loss functions
P. Kar, B. K. Sriperumbudur, P. Jain and H. Karnick
International Conference on Machine Learning, 2013. [pdf]

Ultrahigh dimensional feature screening via RKHS embeddings
K. Balasubramanian, B. K. Sriperumbudur and G. Lebanon
International Conference on Artificial Intelligence and Statistics, 2013. [pdf,supplement]


2012

Optimal kernel choice for large-scale two-sample tests
A. Gretton, B. K. Sriperumbudur, D. Sejdinovic, H. Strathmann, S. Balakrishnan, M. Pontil and K. Fukumizu
Neural Information Processing Systems, 2012. [pdf]

On the empirical estimation of integral probability metrics
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Scholkopf and G. R. G. Lanckriet
Electronic Journal of Statistics, 6: 1550-1599, 2012. [pdf]

Hypothesis testing using pairwise distances and associated kernels
D. Sejdinovic, A. Gretton, B. K. Sriperumbudur and K. Fukumizu
International Conference on Machine Learning, 2012. [pdf]

A proof of convergence of the concave-convex procedure using Zangwill's theory
B. K. Sriperumbudur and G. R. G. Lanckriet
Neural Computation, 24(6): 1391–1407, 2012. [pdf]

Consistency and rates for clustering with DBSCAN
B. K. Sriperumbudur and I. Steinwart
International Conference on Artificial Intelligence and Statistics, 2012. [pdf,supplement]


2011

Learning in Hilbert vs. Banach spaces: A measure embedding viewpoint
B. K. Sriperumbudur, K. Fukumizu and G. R. G. Lanckriet
Neural Information Processing Systems, 2011. [pdf]

A majorization-minimization approach to the sparse generalized eigenvalue problem
B. K. Sriperumbudur, D. A. Torres and G. R. G. Lanckriet
Machine Learning, 85(1):3-39, 2011. [pdf]

Mixture density estimation via Hilbert space embedding of measures
B. K. Sriperumbudur
International Symposium on Information Theory, 2011. [pdf,slides]  

Universality, characteristic kernels and RKHS embedding of measures
B. K. Sriperumbudur, K. Fukumizu and G. R. G. Lanckriet
Journal of Machine Learning Research, 12(Jul): 2389-2410, 2011. [pdf]



2010

Reproducing kernel space embeddings and metrics on probability measures
B. K. Sriperumbudur
Ph. D. Dissertation, UC San Diego, 2010. [pdf]

Non-parametric estimation of integral probability metrics
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, B. Scholkopf and G. R. G. Lanckriet
International Symposium on Information Theory, 2010. [pdf,slides]  

Hilbert space embeddings and metrics on probability measures
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Scholkopf and G. R. G. Lanckriet
Journal of Machine Learning Research, 11(Apr): 1297-1322, 2010. [pdf]

On the relation between universality, characteristic kernels and RKHS embedding of measures
B. K. Sriperumbudur, K. Fukumizu and G. R. G. Lanckriet
International Conference on Artificial Intelligence and Statistics, 2010. [pdf,slides]



2009

Kernel choice and classifiability for RKHS embeddings of probability distributions
B. K. Sriperumbudur, K. Fukumizu, A. Gretton, G. R. G. Lanckriet and B. Scholkopf
Neural Information Processing Systems, 2009. [pdf,slides]
Outstanding student paper award (Honorable mention)

On the convergence of the concave-convex procedure
B. K. Sriperumbudur and G. R. G. Lanckriet
Neural Information Processing Systems, 2009. [pdf]
2nd NIPS Workshop on Optimization for Machine Learning, 2009. [slides]

A fast, consistent kernel two-sample test
A. Gretton, K. Fukumizu, Z. Harchaoui and B. K. Sriperumbudur
Neural Information Processing Systems, 2009. [pdf,supplement]

Discussion of: Brownian distance covariance
A. Gretton, K. Fukumizu, and B. K. Sriperumbudur
Annals of Applied Statistics, 3(4): 1285-1294, 2009. [pdf]

A d.c. programming approach to the sparse generalized eigenvalue problem
B. K. Sriperumbudur, D. A. Torres and G. R. G. Lanckriet [arXiv]
2nd NIPS Workshop on Optimization for Machine Learning, 2009. [pdf,slides]

2008
Characteristic kernels on groups and semigroups
K. Fukumizu,  B. K. Sriperumbudur, A. Gretton and B. Scholkopf
Neural Information Processing Systems, 2008. [pdf]

RKHS representation of measures applied to homogeneity, independence and Fourier optics
B. Scholkopf, B. K. Sriperumbudur, A. Gretton and K. Fukumizu
Oberwolfach Report 30, Mathematisches Forschungsinstitut, Oberwolfach-Walke, Germany, pp. 42-44, 2008. [pdf]

Non-uniform speaker normalization using affine transformation
S. V. Bharath Kumar and S. Umesh
Journal of the Acoustical Society of America, 124(3), pp. 1727-1738, September 2008.

Injective Hilbert space embeddings of probability measures
B. K. Sriperumbudur, A. Gretton, K. Fukumizu, G. R. G. Lanckriet and B. Scholkopf
Conference on Learning Theory, 2008. [pdf,slides] 

Metric embedding for kernel classification rules
B. K. Sriperumbudur, O. Lang and G. R. G. Lanckriet
International Conference on Machine Learning, 2008. [pdf,slides]


2007

The effect of kernel choice on RKHS based statistical tests
B. K. Sriperumbudur, A. Gretton, K. Fukumizu and B. Scholkopf
Representations and Inference on Probability Distributions Workshop, NIPS 2007. [slides]

Finding musically meaningful words using sparse CCA
D. A. Torres, D. Turnbull, B. K. Sriperumbudur, L. Barrington and G. R. G. Lanckriet
Music, Brain and Cognition Workshop, NIPS 2007. [pdf,slides]

Sparse eigen methods by d.c. programming
B. K. Sriperumbudur, D. A. Torres and G. R. G. Lanckriet
International Conference on Machine Learning, 2007. [pdf,slides]

 Nearest neighbor prototyping for sparse and scalable support vector       machines
B. K. Sriperumbudur and G. R. G. Lanckriet
Technical Report, Dept. of ECE, UCSD, February 2007. [pdf]


2006

Study of non-linear frequency warping functions for speaker normalization
S. V. Bharath Kumar, S. Umesh and Rohit Sinha
Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. [pdf]

A framework for parameter optimization in mutual information based registration algorithms
G. Gopalakrishnan, S. V. Bharath Kumar, A. Narayanan and R. Mullick
Proc. of SPIE Medical Imaging, 2006. [pdf]


2005

A fast piece-wise deformable method for multi-modality image registration
G. Gopalakrishnan, S. V. Bharath Kumar, A. Narayanan and R. Mullick
Proc. of Applied Imagery and Pattern Recognition, 2005. [pdf]

Lossless volumetric medical image compression with progressive multi-planar reformatting using 3-D DPCM
V. Nandedkar, S. V. Bharath Kumar and S. Mukhopadhyay
Proc. of National Conference on Image Processing, 2005. (Best Paper Award) [pdf]

Textural content in 3T MR: An image-based marker for Alzheimer's disease
S. V. Bharath Kumar, R. Mullick and U. Patil
Proc. of SPIE Medical Imaging, 2005. [pdf]


2004

Non-uniform speaker normalization using frequency-dependent scaling function
S. V. Bharath Kumar and S. Umesh
Proc. of International Conference on Signal Processing and Communications, 2004. [pdf]

A texture analysis approach for automatic flaw detection in pipelines
S. V. Bharath Kumar and S. Ramaswamy
Proc. of International Conference on Signal Processing and Communications, 2004. [pdf]

A novel progressive thick slab paradigm for volumetric medical image compression and navigation
S. V. Bharath Kumar, S. Mukhopadhyay and V. Nandedkar
Proc. of IEEE International Conference on Image Processing, 2004. [pdf]

Non-uniform speaker normalization using affine transformation
S. V. Bharath Kumar, S. Umesh and R. Sinha
Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, 2004. (rated amongst the top in its review category) [pdf]

An investigation into front-end signal processing for speaker normalization
S. Umesh, R. Sinha and S. V. Bharath Kumar
Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, Montreal, May 2004. [pdf]

Spatial distribution of T2 values in the hippocampus of Alzheimer's disease and control subjects
D. Blezek, S. V. Bharath Kumar, S. Adak, Z. Li, J. Schenck and E. Zimmerman
Twelfth ISMRM Scientific Meeting and Exhibition, 2004. (Poster)


2003

3-D loss-less multi-resolution image compression for medical images
S. Mukhopadhyay, S. V. Bharath Kumar, V. Nandedkar and A. Raparia
RSNA InfoRad presentation, 2003. (Poster)

Block-based conditional entropy coding for medical image compression
S. V. Bharath Kumar, N. Nagaraj, S. Mukhopadhyay and X. Xu
Proc. of SPIE Medical Imaging, 2003. [pdf]


2002

A simple approach to non-uniform vowel normalization
S. Umesh, S. V. Bharath Kumar, M. K. Vinay, R. Sharma and R. Sinha
Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, 2002. [pdf]

A model based approach to non-uniform vowel normalization
S. V. Bharath Kumar
M. Tech Thesis, Department of EE, IIT-K, 2002. [pdf]


1999

Realization of linear time-invariant system stability analyzers
S. V. Bharath Kumar
B. Tech Thesis, Department of ECE, SVU, Tirupati, 1999.