Sayan Mukherjee

Affiliations:
  • Duke University, Durham, NC, USA
  • Columbia University, New York, NY, USA (former)


According to our database1, Sayan Mukherjee authored at least 57 papers between 1995 and 2021.

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Bibliography

2021
The Geometry of Synchronization Problems and Learning Group Actions.
Discret. Comput. Geom., 2021

Accelerating Markov Random Field Inference with Uncertainty Quantification.
CoRR, 2021

Towards Explainable Convolutional Features for Music Audio Modeling.
CoRR, 2021

Statistical robustness of Markov chain Monte Carlo accelerators.
Proceedings of the ASPLOS '21: 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021

2020
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing.
CoRR, 2020

Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series.
CoRR, 2020

Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators.
CoRR, 2020

2019
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators.
CoRR, 2019

Adaptive particle-based approximations of the Gibbs posterior for inverse problems.
CoRR, 2019

2018
Multiple testing with persistent homology.
CoRR, 2018

Subspace Clustering through Sub-Clusters.
CoRR, 2018

Learning Integral Representations of Gaussian Processes.
CoRR, 2018

Scalable Algorithms for Learning High-Dimensional Linear Mixed Models.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

2017
Adaptive Randomized Dimension Reduction on Massive Data.
J. Mach. Learn. Res., 2017

Classical Music Composition Using State Space Models.
CoRR, 2017

Efficient Learning of Graded Membership Models.
CoRR, 2017

Partitioned Tensor Factorizations for Learning Mixed Membership Models.
Proceedings of the 34th International Conference on Machine Learning, 2017

2016
Random walks on simplicial complexes and harmonics.
Random Struct. Algorithms, 2016

Bayesian group factor analysis with structured sparsity.
J. Mach. Learn. Res., 2016

Fast moment estimation for generalized latent Dirichlet models.
CoRR, 2016

2015
The Information Geometry of Mirror Descent.
IEEE Trans. Inf. Theory, 2015

Cumulon: Matrix-Based Data Analytics in the Cloud with Spot Instances.
Proc. VLDB Endow., 2015

Contour trees of uncertain terrains.
Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2015

2014
Cumulon: Cloud-Based Statistical Analysis from Users Perspective.
IEEE Data Eng. Bull., 2014

Fréchet Means for Distributions of Persistence Diagrams.
Discret. Comput. Geom., 2014

A Cheeger-type inequality on simplicial complexes.
Adv. Appl. Math., 2014

2013
A comparative study of covariance selection models for the inference of gene regulatory networks.
J. Biomed. Informatics, 2013

Probabilistic Fréchet Means and Statistics on Vineyards.
CoRR, 2013

Genome-wide identification and predictive modeling of tissue-specific alternative polyadenylation.
Bioinform., 2013

2012
Local homology transfer and stratification learning.
Proceedings of the Twenty-Third Annual ACM-SIAM Symposium on Discrete Algorithms, 2012

2011
Estimating variable structure and dependence in multitask learning via gradients.
Mach. Learn., 2011

2010
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence.
J. Mach. Learn. Res., 2010

Supervised Dimension Reduction Using Bayesian Mixture Modeling.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers.
J. Biomed. Informatics, 2010

Stratification Learning through Homology Inference.
Proceedings of the Manifold Learning and Its Applications, 2010

2009
Comparative study of gene set enrichment methods.
BMC Bioinform., 2009

2008
Modeling Cancer Progression via Pathway Dependencies.
PLoS Comput. Biol., 2008

Localized Sliced Inverse Regression.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Statistical Assessment of MSigDB Gene Sets in Colon Cancer.
Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems, 2008

2007
Characterizing the Function Space for Bayesian Kernel Models.
J. Mach. Learn. Res., 2007

Genomic sweeping for hypermethylated genes.
Bioinform., 2007

Decision Fusion of Circulating Markers for Breast Cancer Detection in Premenopausal Women.
Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 2007

2006
Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation.
PLoS Comput. Biol., 2006

Learning Coordinate Covariances via Gradients.
J. Mach. Learn. Res., 2006

Estimation of Gradients and Coordinate Covariation in Classification.
J. Mach. Learn. Res., 2006

Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization.
Adv. Comput. Math., 2006

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.
Proceedings of the Proceedings 14th International Conference on Intelligent Systems for Molecular Biology 2006, 2006

2005
Permutation Tests for Classification.
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2003
An Analytical Method for Multiclass Molecular Cancer Classification.
SIAM Rev., 2003

Estimating Dataset Size Requirements for Classifying DNA Microarray Data.
J. Comput. Biol., 2003

2002
Choosing Multiple Parameters for Support Vector Machines.
Mach. Learn., 2002

Predicting Signal Peptides with Support Vector Machines.
Proceedings of the Pattern Recognition with Support Vector Machines, 2002

2001
Molecular classification of multiple tumor types.
Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001

Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images.
Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

2000
Feature Selection for SVMs.
Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1996
Automatic generation of RBF networks using wavelets.
Pattern Recognit., 1996

1995
Automatic Generation of GRBF Networks for Visual Learning.
Proceedings of the Procedings of the Fifth International Conference on Computer Vision (ICCV 95), 1995


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