Sayan Mukherjee
15 papers · 2006–2026 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (38) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π§
Keyword Pioneer
π
Keyword Champion
(5)
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Trend Setter
π
Renaissance Researcher
(5)
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Century Club Β· Bronze
ποΈ
Keyword Collector
(95)
Top co-authors
Research topics
Machine Learning
(14)
Core Methods
(10)
Optimization & Theory
(8)
Artificial Intelligence
(7)
Bayesian & Probabilistic Β· Artificial Intelligence
(6)
Bayesian & Probabilistic Β· Machine Learning
(5)
Mathematics & Optimization
(4)
Learning Types
(3)
Mathematics
(3)
Deep Learning
(2)
Optimization
(2)
Computer Science
(1)
Data Science & Analytics
(1)
Healthcare & Medicine
(1)
Application Areas
(1)
Keywords
dimension reduction
(5)
bayesian inference
(5)
supervised learning
(4)
dirichlet process
(3)
gradient estimation
(3)
feature learning
(2)
variable selection
(2)
supervised dimension reduction
(2)
high-dimensional data
(2)
principal component analysis
(1)
variational inference
(1)
spectral clustering
(1)
dimensionality reduction
(1)
manifold learning
(1)
probabilistic modeling
(1)
semi-supervised learning
(1)
uncertainty quantification
(1)
feature selection
(1)
gaussian processes
(1)
matrix factorization
(1)
Papers
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
UAI 2026
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression
NeurIPS 2023
Global optimality of Elman-type RNNs in the mean-field regime
ICML 2023
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes
JMLR 2022
Tight query complexity bounds for learning graph partitions
COLT 2022
Subspace Clustering through Sub-Clusters
JMLR 2021
Adaptive Randomized Dimension Reduction on Massive Data
JMLR 2017
Partitioned Tensor Factorizations for Learning Mixed Membership Models
ICML 2017
Bayesian group factor analysis with structured sparsity
JMLR 2016
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
JMLR 2010
Supervised Dimension Reduction Using Bayesian Mixture Modeling
AISTATS 2010
Localized Sliced Inverse Regression
NeurIPS 2008
Characterizing the Function Space for Bayesian Kernel Models
JMLR 2007
Estimation of Gradients and Coordinate Covariation in Classification
JMLR 2006
Learning Coordinate Covariances via Gradients
JMLR 2006