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Bharath K
Sriperumbudur |
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Home | Publications | Group | CV |
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Preprints (De)-regularized maximum mean discrepancy gradient flow Z. Chen, A. Mustafi, P. Glaser, A. Korba, A. Gretton, and B. K. Sriperumbudur [arxiv] Gradient flows and Riemannian structure in the Gromov-Wasserstein geometry Z. Zhang, Z. Goldfeld, K. Greenewald, Y. Mroueh, and B. K. Sriperumbudur [arxiv] Nystrom kernel Stein discrepancy F. Kalinke, Z. Szabo, and B. K. Sriperumbudur [arxiv] Minimax optimal goodness-of-fit tests with kernel Stein discrepancy O. Hagrass, B. K. Sriperumbudur, and K. Balasubramanian [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. Kuruki [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] 2024 Optimal rates for functional linear regression with general regularization N. Gupta, S. Sivananthan, and B. K. Sriperumbudur Applied and Computational Harmonic Analysis, To appear. [arxiv] On the limits of topological data analysis for statistical inference S. Vishwanath, K. Fukumizu, S. Kuruki 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] Functional linear and single-index models: A unified approach via Gaussian Stein identity K. Balasubramanian, H-G. Muller, and B. K. Sriperumbudur Bernoulli, To appear. [arxiv] 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. Kuruki 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 A
majorization-minimization approach to the sparse
generalized eigenvalue problem Universality,
characteristic kernels and RKHS embedding of measures 2010 Reproducing
kernel space embeddings and metrics on probability measures Non-parametric
estimation of integral probability metrics Hilbert space
embeddings and metrics on probability measures On the
relation between universality, characteristic kernels
and RKHS embedding of measures 2009 Kernel choice
and classifiability for RKHS embeddings of probability
distributions On
the convergence of the concave-convex procedure A
fast, consistent kernel two-sample test Discussion
of: Brownian distance covariance A d.c.
programming approach to the sparse generalized
eigenvalue problem 2008 RKHS
representation of measures applied to homogeneity,
independence and Fourier optics Non-uniform
speaker normalization using affine transformation
Injective Hilbert space embeddings of
probability measures Metric
embedding for kernel classification rules
The effect of kernel choice on RKHS based
statistical tests Finding musically meaningful words using
sparse CCA
Sparse eigen
methods by d.c. programming
Study of non-linear frequency warping
functions for speaker normalization A framework
for parameter optimization in mutual information based
registration algorithms
A fast
piece-wise deformable method for multi-modality image
registration Lossless volumetric medical image
compression with progressive multi-planar reformatting
using 3-D DPCM Textural content in 3T MR: An image-based
marker for Alzheimer's disease
Non-uniform
speaker normalization using frequency-dependent scaling
function A texture
analysis approach for automatic flaw detection in
pipelines A novel
progressive thick slab paradigm for volumetric medical
image compression and navigation Non-uniform
speaker normalization using affine transformation An investigation into front-end signal
processing for speaker normalization D. Blezek, S. V. Bharath Kumar, S. Adak, Z. Li, J. Schenck and E. Zimmerman Twelfth ISMRM Scientific Meeting and Exhibition, 2004. (Poster)
3-D loss-less multi-resolution image
compression for medical images Block-based conditional entropy coding
for medical image compression
A simple approach to non-uniform vowel
normalization A model based approach to non-uniform
vowel normalization
Realization of linear time-invariant
system stability analyzers |