Here are some interesting paper I'm planning to read or browse.
My biggest concern is how effective is the work. It seems currently most are beautiful with formulas, but can not even beat the simplest method.
It's always human who make the world so complicated.
My belief is "The World is Simple!"
Heterogeneous Component Analysis
Shigeyuki Oba, Motoaki Kawanabe, Klaus-Robert Müller, Shin Ishii
Neural characterization in partially observed populations of spiking neurons
Jonathan Pillow, Peter Latham
Probabilistic Matrix Factorization
Ruslan Salakhutdinov, Andriy Mnih
Hidden Common Cause Relations in Relational Learning
Ricardo Silva, Wei Chu, Zoubin Ghahramani
Hierarchical Penalization
Marie Szafranski, Yves Grandvalet, Pierre Morizet-Mahoudeaux
Learning with Transformation Invariant Kernels
Christian Walder
A Spectral Regularization Framework for Multi-Task Structure Learning
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil, Yiming Ying
Supervised Topic Models
David Blei, Jon McAuliffe
Learning Bounds for Domain Adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman
Multi-task Gaussian Process Prediction
Edwin Bonilla, Kian Ming Chai, Chris Williams
Automatic Generation of Social Tags for Music Recommendation
Douglas Eck, Paul Lamere, Thierry Bertin-Mahieux, Stephen Green
Kernel Measures of Conditional Dependence
Kenji Fukumizu, Arthur Gretton, Xiaohai Sun, Bernhard Sch??lkopf
Favorite Theorems: Learning from Natural Proofs
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October Edition
I had a tough choice for my final favorite theorem from the decade
2015-2024. Runners up include Pseudodeterministic Primes and Hardness ...
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