← Authors

Sayan Mukherjee

14 papers · 2006–2023 · 5 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (17) 🐝 Cross-Pollinator (11) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (33) πŸ† Keyword Champion (5) πŸ’Ž Century Club (14) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (88)

Conferences

JMLR (8) ICML (2) NIPS (2) AISTATS (1) COLT (1)

Top co-authors

Qiang Wu (4) Feng Liang (3) Barbara E Engelhardt (2) Zachary C. Holmes (1) Samuel Berchuck (1) Stoyan Georgiev (1) Youngsoo Baek (1) Ding-Xuan Zhou (1) Jan Hannig (1) Justin Guinney (1)

Keywords

dimension reduction (5) bayesian inference (4) supervised learning (4) dirichlet process (3) gradient estimation (3) feature learning (2) variable selection (2) supervised dimension reduction (2) bayesian nonparametrics (1) variational inference (1) dimensionality reduction (1) principal component analysis (1) spectral clustering (1) matrix factorization (1) manifold learning (1) covariance estimation (1) probabilistic modeling (1) feature selection (1) gaussian processes (1) semi-supervised learning (1)

Papers

Global optimality of Elman-type RNNs in the mean-field regime ICML 2023
Asymptotics of Bayesian Uncertainty Estimation in Random Features Regression NIPS 2023
Tight query complexity bounds for learning graph partitions COLT 2022
Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes JMLR 2022
Subspace Clustering through Sub-Clusters JMLR 2021
Partitioned Tensor Factorizations for Learning Mixed Membership Models ICML 2017
Adaptive Randomized Dimension Reduction on Massive Data JMLR 2017
Bayesian group factor analysis with structured sparsity JMLR 2016
Supervised Dimension Reduction Using Bayesian Mixture Modeling AISTATS 2010
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence JMLR 2010
Localized Sliced Inverse Regression NIPS 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