S. Mukherjee

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Statistical inference for dynamical systems: A review

K. Mcgoff, S. Mukherjee, N. Pillai, 2012, 1204.6265.

S. Mukherjee, Feng Liang, 2007 .

The Information Geometry of Mirror Descent

Sayan Mukherjee, Garvesh Raskutti, S. Mukherjee, 2013, IEEE Transactions on Information Theory.

Feature reduction and hierarchy of classifiers for fast object detection in video images

Sayan Mukherjee, Thomas Serre, Tomaso A. Poggio, 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

Probability measures on the space of persistence diagrams

S. Mukherjee, Yuriy Mileyko, J. Harer, 2011 .

Approximations of Markov Chains and High-Dimensional Bayesian Inference

S. Mukherjee, D. Dunson, J. Johndrow, 2015 .

Probabilistic Fréchet Means and Statistics on Vineyards

Sayan Mukherjee, Paul Bendich, John Harer, 2013, ArXiv.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES

T. Poggio, S. Mukherjee, B. Heisele, 2001 .

Topological Consistency via Kernel Estimation

S. Mukherjee, Jonathan E. Taylor, O. Bobrowski, 2014, 1407.5272.

Topological Summaries of Tumor Images Improve Prediction of Disease Free Survival in Glioblastoma Multiforme

S. Mukherjee, R. Rabadán, Lorin Crawford, 2016 .

Bagging Regularizes

T. Poggio, S. Mukherjee, R. Rifkin, 2002 .

Persistent Homology Transform for Modeling Shapes and Surfaces

S. Mukherjee, Katharine Turner, D. Boyer, 2013, 1310.1030.

Risk bounds for mixture density estimation

D. Panchenko, S. Mukherjee, A. Rakhlin, 2005 .

Multivariate Density Estimation: an SVM Approach

V. Vapnik, S. Mukherjee, 1999 .

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence

Sayan Mukherjee, Justin Guinney, Mauro Maggioni, 2010, J. Mach. Learn. Res..

Characterizing the Function Space for Bayesian Kernel Models

Sayan Mukherjee, Feng Liang, Natesh S. Pillai, 2007, J. Mach. Learn. Res..

A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer.

S. Mukherjee, M. West, H. Dressman, 2006, The New England journal of medicine.

Localized Sliced Inverse Regression

Sayan Mukherjee, Feng Liang, Qiang Wu, 2008, NIPS.

Learning gradients : predictive models that infer geometry and dependence

S. Mukherjee, M. Maggioni, J. Guinney, 2007 .

Consistency of maximum likelihood estimation for some dynamical systems

A. Nobel, K. Mcgoff, S. Mukherjee, 2013, 1306.5603.

Approximations of Markov Chains and Bayesian Inference

S. Mukherjee, D. Dunson, J. Johndrow, 2015 .

Bayesian group factor analysis with structured sparsity

Sayan Mukherjee, Chuan Gao, Shiwen Zhao, 2016, J. Mach. Learn. Res..

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma.

S. Mukherjee, F. Medeiros, S. Berchuck, 2019, 1908.09195.

Bayesian group latent factor analysis with structured sparse priors

Sayan Mukherjee, Shiwen Zhao, Barbara E. Engelhardt, 2014 .

Bayesian group latent factor analysis with structured sparsity

Sayan Mukherjee, Shiwen Zhao, Barbara E Engelhardt, 2014, 1411.2698.

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

Lawrence A. David, S. Mukherjee, J. Silverman, 2018, bioRxiv.

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles

Sayan Mukherjee, Justin Guinney, Andrea Bild, 2006, ISMB.

FEATURES THAT DIFFERENTIATE CLASSES OF 3D SHAPES

S. Mukherjee, Lorin Crawford, D. Boyer, 2020 .

Gibbs posterior convergence and the thermodynamic formalism

A. Nobel, K. Mcgoff, S. Mukherjee, 2019, The Annals of Applied Probability.

University of Birmingham Dynamical systems as temporal feature spaces

S. Mukherjee, P. Tiňo, Tino, 2020 .

A geometric approach for inference on graphical models

S. Mukherjee, R. Wolpert, S. Lunagomez, 2009 .

Subspace-Induced Gaussian Processes

S. Mukherjee, Zilong Tan, S. Mukherjee, 2018 .

Partial Factor Modeling: Predictor-Dependent Shrinkage for Linear Regression

S. Mukherjee, C. Carvalho, P. R. Hahn, 2013 .

The topology of probability distributions on manifolds

S. Mukherjee, O. Bobrowski, S. Mukherjee, 2013, 1307.1123.

Kernel Sliced Inverse Regression: Regularization and Consistency

S. Mukherjee, Feng Liang, Qiang Wu, 2013 .

How Many Directions Determine a Shape and other Sufficiency Results for Two Topological Transforms

Sayan Mukherjee, Katharine Turner, Justin Curry, 2018, Transactions of the American Mathematical Society, Series B.

Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits

S. Mukherjee, Sayan Mukherjee, Xiaoping Zhou, 2017, bioRxiv.

A phylogenetic transform enhances analysis of compositional microbiota data

Sayan Mukherjee, Justin D. Silverman, Lawrence A. David, 2016 .

Learning Subspaces of Different Dimensions

Lizhen Lin, Lek-Heng Lim, Sayan Mukherjee, 2021, Journal of Computational and Graphical Statistics.

Predictor-dependent shrinkage for linear regression via partial factor modeling

S. Mukherjee, C. Carvalho, P. R. Hahn, 2010, 1011.3725.

Geometric Representations of Random Hypergraphs

Edoardo M. Airoldi, Robert L. Wolpert, Sayan Mukherjee, 2009 .

Geometric Representations of Hypergraphs for Prior Specification and Posterior Sampling

Edoardo M. Airoldi, Simon Lunagomez, Robert L. Wolpert, 2009 .

Phylofactorization: a graph-partitioning algorithm to identify phylogenetic scales of ecological data

Sayan Mukherjee, James T. Morton, Justin D. Silverman, 2017, bioRxiv.

Differential expression analysis for RNAseq using Poisson mixed models

S. Mukherjee, Sayan Mukherjee, Xiaoping Zhou, 2016, bioRxiv.

On the geometric properties of finite mixture models

S. Mukherjee, Michele Caprio, S. Mukherjee, 2020 .

Expected return time to the initial state for biochemical systems with linear cyclic chains: unidirectional and bidirectional reactions

R. K. De, S. Mukherjee, D. Ghosh, 2018, Sādhanā.

Conditional Independence Models via Filtrations

S. Mukherjee, R. Wolpert, S. Lunagomez, 2009 .

Statistical Robustness of MCMC Accelerators

S. Mukherjee, X. Zhang, A. Lebeck, 2020 .

Triangles in graphs without bipartite suspensions

S. Mukherjee, D. Mubayi, 2020, Discret. Math..

Random Lie Brackets that Induce Torsion: A Model for Noisy Vector Fields

S. Mukherjee, Didong Li, 2020, 2007.07309.

Reachability for Updatable Timed Automata made faster and more effective

S. Mukherjee, P. Gastin, B. Srivathsan, 2020, FSTTCS.

Concerning three classes of non-Diophantine arithmetics

S. Mukherjee, Michele Caprio, A. Aveni, 2021, Involve, a Journal of Mathematics.

Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation

Sayan Mukherjee, Terrence S. Furey, Zhong Wang, 2006, PLoS Comput. Biol..

FINITE MIXTURE MODELS: A BRIDGE WITH STOCHASTIC GEOMETRY AND CHOQUET THEORY

S. Mukherjee, Michele Caprio, S. Mukherjee, 2022 .

Ergodic Theorems for Dynamic Imprecise Probability Kinematics

S. Mukherjee, Michele Caprio, 2020, 2003.06502.

Finite mixture models: a bridge with stochastic geometry and Choquet theory

S. Mukherjee, Michele Caprio, 2020, 2002.08409.

A new stability theorem for the expansion of cliques

S. Mukherjee, Xizhi Liu, 2020, 2006.04848.

Probabilistic Fréchet means for time varying persistence diagrams

S. Mukherjee, J. Harer, J. Mattingly, 2013, 1307.6530.

A Statistical Analysis of Compositional Surveys

S. Mukherjee, J. Silverman, J. Letourneau, 2022, 2201.03616.

The accuracy of absolute differential abundance analysis from 1 relative count data

S. Mukherjee, Sayan Mukherjee, Kimberly E. Roche, 2022 .

Finite admixture models: a bridge with stochastic geometry and Choquet theory

S. Mukherjee, Michele Caprio, 2020, 2002.08409.

Ergodic theorems for lower probability kinematics.

S. Mukherjee, Michele Caprio, 2020 .

Representing Fields without Correspondences: the Lifted Euler Characteristic Transform

S. Mukherjee, Henry Kirveslahti, 2021, 2111.04788.

Maximum H-free subgraphs

S. Mukherjee, D. Mubayi, 2019 .

M ay 2 01 9 Maximum H-free subgraphs

S. Mukherjee, D. Mubayi, 2019 .

Nonparametric Bayesian Kernel Models

S. Mukherjee, Feng Liang, S. Mukherjee, 2007 .

Neural Sequence Transformation

Nobuyuki Umetani, Binh-Son Hua, Daniel Meister, 2021, Comput. Graph. Forum.

Functional Data Analysis using a Topological Summary Statistic: the Smooth Euler Characteristic Transform

Sayan Mukherjee, Lorin Crawford, Andrew X. Chen, 2016 .

Genomic sweeping for hypermethylated genes

T. Furey, S. Mukherjee, S. Murphy, 2007, Bioinform..

Comparative study of gene set enrichment methods

Annarita D'Addabbo, Nicola Ancona, Rosalia Maglietta, 2009, BMC Bioinformatics.

Estimating Rates of Progression and Predicting Future Visual Fields in Glaucoma Using a Deep Variational Autoencoder

Sayan Mukherjee, Felipe A. Medeiros, Samuel I. Berchuck, 2019, Scientific Reports.

Random walks on simplicial complexes and harmonics†

Sayan Mukherjee, John Steenbergen, S. Mukherjee, 2013, Random Struct. Algorithms.

Genome-wide identification and predictive modeling of tissue-specific alternative polyadenylation

Sayan Mukherjee, Jun Zhu, Uwe Ohler, 2013, Bioinform..

A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

Bruce Wang, Tingran Gao, Lorin Crawford, 2019, bioRxiv.

A topological data analytic approach for discovering biophysical signatures in protein dynamics

S. Mukherjee, Lorin Crawford, B. Rubenstein, 2021, bioRxiv.

On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers

Annarita D'Addabbo, Nicola Ancona, Rosalia Maglietta, 2010, J. Biomed. Informatics.

The accuracy of absolute differential abundance analysis from relative count data

S. Mukherjee, Kimberly E. Roche, S. Mukherjee, 2021, bioRxiv.

Modeling Cancer Progression via Pathway Dependencies

Sayan Mukherjee, Justin Guinney, Elena J. Edelman, 2008, PLoS Comput. Biol..

Synchrony and idiosyncrasy in the gut microbiome of wild baboons

Laura E. Grieneisen, S. Mukherjee, Ran Blekhman, 2021, Nature Ecology & Evolution.

Statistical Assessment of MSigDB Gene Sets in Colon Cancer

Sayan Mukherjee, Annarita D'Addabbo, Nicola Ancona, 2008, KES.

Retraction: A genomic strategy to refine prognosis in early-stage non-small-cell lung cancer. N Engl J Med 2006;355:570-80.

S. Mukherjee, M. West, H. Dressman, 2011, The New England journal of medicine.

The Geometry of Synchronization Problems and Learning Group Actions

Sayan Mukherjee, Tingran Gao, Jacek Brodzki, 2016, Discrete & Computational Geometry.

Learning gradients : predictive models that infer geometry and dependence

S. Mukherjee, M. Maggioni, J. Guinney, 2007 .

Fast principal components analysis reveals convergent evolution of ADH1B gene in Europe and East Asia

Sayan Mukherjee, Alkes L. Price, Gaurav Bhatia, 2015, bioRxiv.

A Statistical Analysis of Compositional Surveys

Lawrence A. David, S. Mukherjee, J. Silverman, 2022 .

A Sheaf-Theoretic Construction of Shape Space

S. Mukherjee, J. Curry, Shreya Arya, 2022, ArXiv.

Estimation of Gradients and Coordinate Covariation in Classification

Sayan Mukherjee, Qiang Wu, S. Mukherjee, 2006, J. Mach. Learn. Res..

On Stability and Concentration of Measure

T. Poggio, S. Mukherjee, A. Rakhlin, 2004 .

On the expected number of components in a finite admixture model.

S. Mukherjee, Michele Caprio, S. Mukherjee, 2020 .

A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors

Darryl Shibata, Andrew S. Allen, Kevin A. Murgas, 2021, Bioinform..

Permutation Tests for Classification

Sayan Mukherjee, Dmitry Panchenko, Polina Golland, 2005, COLT.

Learning Integral Representations of Gaussian Processes pdf

Sayan Mukherjee, Zilong Tan, S. Mukherjee, 2018, ArXiv.

Adaptive Randomized Dimension Reduction on Massive Data

Sayan Mukherjee, Barbara E. Engelhardt, Gregory Darnell, 2015, J. Mach. Learn. Res..

Local homology transfer and stratification learning

Sayan Mukherjee, Bei Wang, Paul Bendich, 2012, SODA.

The Unsupervised Acquisition of a Lexicon from Continuous Speech

T. Poggio, S. Mukherjee, B. Heisele, 1995, ArXiv.

Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes

Sayan Mukherjee, Kimberly Roche, Justin D. Silverman, 2019, J. Mach. Learn. Res..

Partitioned Tensor Factorizations for Learning Mixed Membership Models

Sayan Mukherjee, Zilong Tan, S. Mukherjee, 2017, ICML.

Efficient Learning of Graded Membership Models pdf

Sayan Mukherjee, Zilong Tan, S. Mukherjee, 2017, ArXiv.

The Information Geometry of Mirror Descent

Sayan Mukherjee, Garvesh Raskutti, S. Mukherjee, 2013, IEEE Transactions on Information Theory.

Measuring and mitigating PCR bias in microbiota datasets

Justin D. Silverman, Lawrence A. David, Sayan Mukherjee, 2021, PLoS Comput. Biol..

Fréchet Means for Distributions of Persistence Diagrams

Sayan Mukherjee, John Harer, Katharine Turner, 2012, Discrete & Computational Geometry.

Learning Coordinate Covariances via Gradients

Sayan Mukherjee, Ding-Xuan Zhou, Ding-Xuan Zhou, 2006, J. Mach. Learn. Res..

Classical Music Composition Using State Space Models pdf

Sayan Mukherjee, Anna K. Yanchenko, S. Mukherjee, 2017, ArXiv.

Adaptive particle-based approximations of the Gibbs posterior for inverse problems pdf

Sayan Mukherjee, Harbir Antil, Wilkins Aquino, 2019, ArXiv.

A Cheeger-Type Inequality on Simplicial Complexes pdf

Caroline J. Klivans, Sayan Mukherjee, John Steenbergen, 2012, Adv. Appl. Math..

Topological Data Analysis and Machine Learning Theory

Sayan Mukherjee, Mikhail Belkin, Brittany Terese Fasy, 2012 .

A Grover Search-Based Algorithm for the List Coloring Problem

S. Mukherjee, 2021, IEEE Transactions on Quantum Engineering.

Estimating variable structure and dependence in multitask learning via gradients

Sayan Mukherjee, Justin Guinney, Qiang Wu, 2011, Machine Learning.

Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data

James T. Morton, S. Mukherjee, R. Plowright, 2019, Ecological Monographs.

Multiple testing with persistent homology pdf

Sayan Mukherjee, Mikael Vejdemo-Johansson, S. Mukherjee, 2018, ArXiv.

Tur\'an Numbers of Hypergraph Suspensions of Even Cycles

Sayan Mukherjee, S. Mukherjee, 2021, 2101.06743.

Multivariate Density Estimation: a Support Vector Machine Approach

Vladimir Vapnik, Sayan D. Mukherjee, V. Vapnik, 1999 .

Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces

Felipe A. Medeiros, Samuel I. Berchuck, Sayan Mukherjee, 2019, Bayesian Analysis.

Statistical robustness of Markov chain Monte Carlo accelerators

Xiangyu Zhang, Alvin R. Lebeck, Ramin Bashizade, 2021, ASPLOS.

Thermal Entanglement of a Spin-1/2 Transverse Ising Model—Heisenberg Model on a Balanced DNA Helix

S. Mukherjee, Sayan Das, S. S. Roy, 2021, Studies in Autonomic, Data-driven and Industrial Computing.

Stratification Learning through Homology Inference

Sayan Mukherjee, Bei Wang, Paul Bendich, 2010, AAAI Fall Symposium: Manifold Learning and Its Applications.

Ergodic theorems for dynamic imprecise probability kinematics

S. Mukherjee, Michele Caprio, S. Mukherjee, 2020, Int. J. Approx. Reason..

Fast Moment Estimation for Generalized Latent Dirichlet Models

Sayan Mukherjee, David B. Dunson, Shiwen Zhao, 2016, Journal of the American Statistical Association.

Multiple testing with persistent homology

S. Mukherjee, Mikael Vejdemo-Johansson, S. Mukherjee, 2018, ArXiv.

Sufficient statistics for shapes and surfaces

S. Mukherjee, Katharine Turner, D. Boyer, 2013 .

At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing pdf

Sayan Mukherjee, Ziyang Ding, S. Mukherjee, 2020, ArXiv.

Supervised Dimension Reduction Using Bayesian Mixture Modeling

Sayan Mukherjee, Kai Mao, Feng Liang, 2010, AISTATS.

Decision Fusion of Circulating Markers for Breast Cancer Detection in Premenopausal Women

Sayan Mukherjee, Joseph Y. Lo, Jeffrey R. Marks, 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series pdf

Sayan Mukherjee, Anna K. Yanchenko, S. Mukherjee, 2020, ArXiv.

Consistency of maximum likelihood estimation for some dynamical systems

A. Nobel, K. Mcgoff, S. Mukherjee, 2013, 1306.5603.

Regularized Bayesian best response learning in finite games

S. Mukherjee, Souvik Roy, 2021, 2111.13687.

A comparative study of covariance selection models for the inference of gene regulatory networks

Sayan Mukherjee, Nicola Ancona, Roberto Anglani, 2013, J. Biomed. Informatics.