Sayan Mukherjee

Publication Activity (10 Years)

Years Active: 2000-2024
Publications (10 Years): 41

Top Topics

Latent Dirichlet

Information Geometry

Bayesian Hierarchical Model

Top Venues

J. Mach. Learn. Res.

PLoS Comput. Biol.

Discret. Comput. Geom.

Publications

Eric Roldán Roa, Erika Berenice Roldan-Roa, Doris Kristina Raave, Jo Van Herwegen, Nina Polytimou, Sayan Mukherjee, Tyler Colasante, Tina Malti, Julia Mori
Play My Math: Second Development Cycle of an EdTech Tool Supporting the Teaching and Learning of Fractions Through Music in Algebraic Notation. ICITL (2) (2024)
Henry Kirveslahti, Sayan Mukherjee
Representing fields without correspondences: the lifted Euler characteristic transform. J. Appl. Comput. Topol. 8 (1) (2024)
Youngsoo Baek, Samuel Berchuck, Sayan Mukherjee
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression. NeurIPS (2023)
Michele Caprio, Sayan Mukherjee
Concentration Inequalities and Optimal Number of Layers for Stochastic Deep Neural Networks. IEEE Access 11 (2023)
Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
Global optimality of Elman-type RNNs in the mean-field regime. ICML (2023)
Michele Caprio, Sayan Mukherjee
Ergodic theorems for dynamic imprecise probability kinematics. Int. J. Approx. Reason. 152 (2023)
Shreya Arya, Justin Curry, Sayan Mukherjee
A Sheaf-Theoretic Construction of Shape Space. CoRR (2022)
Wai-Shing Tang, Gabriel Monteiro da Silva, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, Lorin Crawford
A topological data analytic approach for discovering biophysical signatures in protein dynamics. PLoS Comput. Biol. 18 (5) (2022)
Kimberly Roche, Sayan Mukherjee
The accuracy of absolute differential abundance analysis from relative count data. PLoS Comput. Biol. 18 (7) (2022)
Michele Caprio, Sayan Mukherjee
Concentration inequalities and optimal number of layers for stochastic deep neural networks. CoRR (2022)
Justin D. Silverman, Kimberly Roche, Zachary C. Holmes, Lawrence A. David, Sayan Mukherjee
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes. J. Mach. Learn. Res. 23 (2022)
Brian St. Thomas, Kisung You, Lizhen Lin, Lek-Heng Lim, Sayan Mukherjee
Learning Subspaces of Different Dimensions. J. Comput. Graph. Stat. 31 (1) (2022)
Ramin Bashizade, Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
Accelerating Markov Random Field Inference with Uncertainty Quantification. CoRR (2021)
Tingran Gao, Jacek Brodzki, Sayan Mukherjee
The Geometry of Synchronization Problems and Learning Group Actions. Discret. Comput. Geom. 65 (1) (2021)
Weiwei Li, Jan Hannig, Sayan Mukherjee
Subspace Clustering through Sub-Clusters. J. Mach. Learn. Res. 22 (2021)
Anna K. Yanchenko, Mohammadreza Soltani, Robert J. Ravier, Sayan Mukherjee, Vahid Tarokh
Towards Explainable Convolutional Features for Music Audio Modeling. CoRR (2021)
Justin D. Silverman, Rachael J. Bloom, Sharon Jiang, Heather K. Durand, Eric Dallow, Sayan Mukherjee, Lawrence A. David
Measuring and mitigating PCR bias in microbiota datasets. PLoS Comput. Biol. 17 (7) (2021)
Xiangyu Zhang, Ramin Bashizade, Yicheng Wang, Sayan Mukherjee, Alvin R. Lebeck
Statistical robustness of Markov chain Monte Carlo accelerators. ASPLOS (2021)
Kevin A. Murgas, Yanlin Ma, Lidea K. Shahidi, Sayan Mukherjee, Andrew S. Allen, Darryl Shibata, Marc D. Ryser
A Bayesian hierarchical model to estimate DNA methylation conservation in colorectal tumors. Bioinform. 38 (1) (2021)
Xiangyu Zhang, Ramin Bashizade, Yicheng Wang, Cheng Lyu, Sayan Mukherjee, Alvin R. Lebeck
Beyond Application End-Point Results: Quantifying Statistical Robustness of MCMC Accelerators. CoRR (2020)
Ziyang Ding, Sayan Mukherjee
At the Intersection of Deep Sequential Model Framework and State-space Model Framework: Study on Option Pricing. CoRR (2020)
Anna K. Yanchenko, Sayan Mukherjee
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series. CoRR (2020)
Zilong Zou, Sayan Mukherjee, Harbir Antil, Wilkins Aquino
Adaptive particle-based approximations of the Gibbs posterior for inverse problems. CoRR (2019)
Xiangyu Zhang, Sayan Mukherjee, Alvin R. Lebeck
A Case for Quantifying Statistical Robustness of Specialized Probabilistic AI Accelerators. CoRR (2019)
Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models. UAI (2018)
Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
Scalable Algorithms for Learning High-Dimensional Linear Mixed Models. CoRR (2018)
Weiwei Li, Jan Hannig, Sayan Mukherjee
Subspace Clustering through Sub-Clusters. CoRR (2018)
Mikael Vejdemo-Johansson, Sayan Mukherjee
Multiple testing with persistent homology. CoRR (2018)
Zilong Tan, Sayan Mukherjee
Learning Integral Representations of Gaussian Processes. CoRR (2018)
Zilong Tan, Sayan Mukherjee
Efficient Learning of Graded Membership Models. CoRR (2017)
Zilong Tan, Sayan Mukherjee
Partitioned Tensor Factorizations for Learning Mixed Membership Models. ICML (2017)
Gregory Darnell, Stoyan Georgiev, Sayan Mukherjee, Barbara E. Engelhardt
Adaptive Randomized Dimension Reduction on Massive Data. J. Mach. Learn. Res. 18 (2017)
Anna K. Yanchenko, Sayan Mukherjee
Classical Music Composition Using State Space Models. CoRR (2017)
Shiwen Zhao, Barbara E. Engelhardt, Sayan Mukherjee, David B. Dunson
Fast moment estimation for generalized latent Dirichlet models. CoRR (2016)
Sayan Mukherjee, John Steenbergen
Random walks on simplicial complexes and harmonics. Random Struct. Algorithms 49 (2) (2016)
Shiwen Zhao, Chuan Gao, Sayan Mukherjee, Barbara E. Engelhardt
Bayesian group factor analysis with structured sparsity. J. Mach. Learn. Res. 17 (2016)
Tingran Gao, Jacek Brodzki, Sayan Mukherjee
The Geometry of Synchronization Problems and Learning Group Actions. CoRR (2016)
Wuzhou Zhang, Pankaj K. Agarwal, Sayan Mukherjee
Contour trees of uncertain terrains. SIGSPATIAL/GIS (2015)
Garvesh Raskutti, Sayan Mukherjee
The Information Geometry of Mirror Descent. GSI (2015)
Botong Huang, Nicholas W. D. Jarrett, Shivnath Babu, Sayan Mukherjee, Jun Yang
Cumulon: Matrix-Based Data Analytics in the Cloud with Spot Instances. Proc. VLDB Endow. 9 (3) (2015)
Garvesh Raskutti, Sayan Mukherjee
The Information Geometry of Mirror Descent. IEEE Trans. Inf. Theory 61 (3) (2015)
John Steenbergen, Caroline J. Klivans, Sayan Mukherjee
A Cheeger-type inequality on simplicial complexes. Adv. Appl. Math. 56 (2014)
Katharine Turner, Yuriy Mileyko, Sayan Mukherjee, John Harer
Fréchet Means for Distributions of Persistence Diagrams. Discret. Comput. Geom. 52 (1) (2014)
Botong Huang, Nicholas W. D. Jarrett, Shivnath Babu, Sayan Mukherjee, Jun Yang
Cumulon: Cloud-Based Statistical Analysis from Users Perspective. IEEE Data Eng. Bull. 37 (3) (2014)
Patrizia F. Stifanelli, Teresa Maria Creanza, Roberto Anglani, Vania C. Liuzzi, Sayan Mukherjee, Francesco P. Schena, Nicola Ancona
A comparative study of covariance selection models for the inference of gene regulatory networks. J. Biomed. Informatics 46 (5) (2013)
Garvesh Raskutti, Sayan Mukherjee
The Information Geometry of Mirror Descent. CoRR (2013)
Dina Hafez, Ting Ni, Sayan Mukherjee, Jun Zhu, Uwe Ohler
Genome-wide identification and predictive modeling of tissue-specific alternative polyadenylation. Bioinform. 29 (13) (2013)
Elizabeth Munch, Paul Bendich, Katharine Turner, Sayan Mukherjee, Jonathan Mattingly, John Harer
Probabilistic Fréchet Means and Statistics on Vineyards. CoRR (2013)
Paul Bendich, Bei Wang, Sayan Mukherjee
Local homology transfer and stratification learning. SODA (2012)
Sayan Mukherjee, Caroline J. Klivans, John Steenbergen
A Cheeger-Type Inequality on Simplicial Complexes CoRR (2012)
Justin Guinney, Qiang Wu, Sayan Mukherjee
Estimating variable structure and dependence in multitask learning via gradients. Mach. Learn. 83 (3) (2011)
Paul Bendich, Sayan Mukherjee, Bei Wang
Stratification Learning through Homology Inference. AAAI Fall Symposium: Manifold Learning and Its Applications (2010)
Rosalia Maglietta, Angela Distaso, Ada Piepoli, Orazio Palumbo, Massimo Carella, Annarita D'Addabbo, Sayan Mukherjee, Nicola Ancona
On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers. J. Biomed. Informatics 43 (3) (2010)
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence. J. Mach. Learn. Res. 11 (2010)
Kai Mao, Feng Liang, Sayan Mukherjee
Supervised Dimension Reduction Using Bayesian Mixture Modeling. AISTATS (2010)
Luca Abatangelo, Rosalia Maglietta, Angela Distaso, Annarita D'Addabbo, Teresa Maria Creanza, Sayan Mukherjee, Nicola Ancona
Comparative study of gene set enrichment methods. BMC Bioinform. 10 (2009)
Angela Distaso, Luca Abatangelo, Rosalia Maglietta, Teresa Maria Creanza, Ada Piepoli, Massimo Carella, Annarita D'Addabbo, Sayan Mukherjee, Nicola Ancona
Statistical Assessment of MSigDB Gene Sets in Colon Cancer. KES (2) (2008)
Elena J. Edelman, Justin Guinney, Jen-Tsan A. Chi, Phillip G. Febbo, Sayan Mukherjee
Modeling Cancer Progression via Pathway Dependencies. PLoS Comput. Biol. 4 (2) (2008)
Qiang Wu, Sayan Mukherjee, Feng Liang
Localized Sliced Inverse Regression. NIPS (2008)
Natesh S. Pillai, Qiang Wu, Feng Liang, Sayan Mukherjee, Robert L. Wolpert
Characterizing the Function Space for Bayesian Kernel Models. J. Mach. Learn. Res. 8 (2007)
Liang Goh, Susan K. Murphy, Sayan Mukherjee, Terrence S. Furey
Genomic sweeping for hypermethylated genes. Bioinform. 23 (3) (2007)
Jonathan L. Jesneck, Sayan Mukherjee, Loren W. Nolte, Anna E. Lokshin, Jeffrey R. Marks, Joseph Y. Lo
Decision Fusion of Circulating Markers for Breast Cancer Detection in Premenopausal Women. BIBE (2007)
Sayan Mukherjee, Qiang Wu
Estimation of Gradients and Coordinate Covariation in Classification. J. Mach. Learn. Res. 7 (2006)
Sayan Mukherjee, Partha Niyogi, Tomaso A. Poggio, Ryan M. Rifkin
Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization. Adv. Comput. Math. 25 (1-3) (2006)
Elena J. Edelman, Alessandro Porrello, Justin Guinney, Bala Balakumaran, Andrea Bild, Phillip G. Febbo, Sayan Mukherjee
Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. ISMB (Supplement of Bioinformatics) (2006)
Zhong Wang, Huntington F. Willard, Sayan Mukherjee, Terrence S. Furey
Evidence of Influence of Genomic DNA Sequence on Human X Chromosome Inactivation. PLoS Comput. Biol. 2 (9) (2006)
Sayan Mukherjee, Ding-Xuan Zhou
Learning Coordinate Covariances via Gradients. J. Mach. Learn. Res. 7 (2006)
Polina Golland, Feng Liang, Sayan Mukherjee, Dmitry Panchenko
Permutation Tests for Classification. COLT (2005)
Ryan M. Rifkin, Sayan Mukherjee, Pablo Tamayo, Sridhar Ramaswamy, Chen-Hsiang Yeang, Michael Angelo, Michael Reich, Tomaso A. Poggio, Eric S. Lander, Todd R. Golub, Jill P. Mesirov
An Analytical Method for Multiclass Molecular Cancer Classification. SIAM Rev. 45 (4) (2003)
Sayan Mukherjee, Pablo Tamayo, Simon Rogers, Ryan M. Rifkin, Anna Engle, Colin Campbell, Todd R. Golub, Jill P. Mesirov
Estimating Dataset Size Requirements for Classifying DNA Microarray Data. J. Comput. Biol. 10 (2) (2003)
Neelanjan Mukherjee, Sayan Mukherjee
Predicting Signal Peptides with Support Vector Machines. SVM (2002)
Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
Choosing Multiple Parameters for Support Vector Machines. Mach. Learn. 46 (1-3) (2002)
Bernd Heisele, Thomas Serre, Sayan Mukherjee, Tomaso A. Poggio
Feature Reduction and Hierarchy of Classifiers for Fast Object Detection in Video Images. CVPR (2) (2001)
Chen-Hsiang Yeang, Sridhar Ramaswamy, Pablo Tamayo, Sayan Mukherjee, Ryan M. Rifkin, Michael Angelo, Michael Reich, Eric S. Lander, Jill P. Mesirov, Todd R. Golub
Molecular classification of multiple tumor types. ISMB (Supplement of Bioinformatics) (2001)
Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimiliano Pontil, Tomaso A. Poggio, Vladimir Vapnik
Feature Selection for SVMs. NIPS (2000)