Papers
4,025 papers found
Linear-time training of nonlinear low-dimensional embeddings
Max Vladymyrov, Miguel Carreira-Perpinan
Loopy Belief Propagation in the Presence of Determinism
David Smith, Vibhav Gogate
Low-Rank Spectral Learning
Alex Kulesza, N. Raj Rao, Satinder Singh
Mixed Graphical Models via Exponential Families
Eunho Yang, Yulia Baker, Pradeep Ravikumar et al.
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi et al.
Non-Asymptotic Analysis of Relational Learning with One Network
Peng He, Changshui Zhang
Nonparametric estimation and testing of exchangeable graph models
Justin Yang, Christina Han, Edoardo Airoldi
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
Matthew Hoffman, Bobak Shahriari, Nando Freitas
On Estimating Causal Effects based on Supplemental Variables
Takahiro Hayashi, Manabu Kuroki
Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion
Mathieu Blondel, Yotaro Kubo, Ueda Naonori
On the Testability of Models with Missing Data
Karthika Mohan, Judea Pearl
Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors
Junya Honda, Akimichi Takemura
PAC-Bayesian Collective Stability
Ben London, Bert Huang, Ben Taskar et al.
PAC-Bayesian Theory for Transductive Learning
Luc Bégin, Pascal Germain, François Laviolette et al.
Pan-sharpening with a Bayesian nonparametric dictionary learning model
Xinghao Ding, Yiyong Jiang, Yue Huang et al.
Path Thresholding: Asymptotically Tuning-Free High-Dimensional Sparse Regression
Divyanshu Vats, Richard Baraniuk
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Philipp Hennig, Søren Hauberg
Random Bayesian networks with bounded indegree
Eunice Yuh-Jie Chen, Judea Pearl
Recovering Distributions from Gaussian RKHS Embeddings
Motonobu Kanagawa, Kenji Fukumizu
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions
Asaf Noy, Koby Crammer
Robust learning of inhomogeneous PMMs
Ralf Eggeling, Teemu Roos, Petri Myllymäki et al.
Robust Stochastic Principal Component Analysis
John Goes, Teng Zhang, Raman Arora et al.
Scalable Collaborative Bayesian Preference Learning
Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger
Scalable Variational Bayesian Matrix Factorization with Side Information
Yong-Deok Kim, Seungjin Choi