Papers
Jointly Informative Feature Selection
Leonidas Lefakis, Francois Fleuret
Incremental Tree-Based Inference with Dependent Normalized Random Measures
Juho Lee, Seungjin Choi
Towards building a Crowd-Sourced Sky Map
Dustin Lang, David Hogg, Bernhard Schölkopf
Computational Education using Latent Structured Prediction
Tanja Käser, Alexander Schwing, Tamir Hazan et al.
Fugue: Slow-Worker-Agnostic Distributed Learning for Big Models on Big Data
Abhimanu Kumar, Alex Beutel, Qirong Ho et al.
Low-Rank Spectral Learning
Alex Kulesza, N. Raj Rao, Satinder Singh
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection
Jyri Kivinen, Chris Williams, Nicolas Heess
Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold
Franz Király, Martin Ehler
Scalable Variational Bayesian Matrix Factorization with Side Information
Yong-Deok Kim, Seungjin Choi
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data
Do-kyum Kim, Matthew Der, Lawrence Saul
Scalable Collaborative Bayesian Preference Learning
Mohammad Emtiyaz Khan, Young Jun Ko, Matthias Seeger
Collaborative Ranking for Local Preferences
Berk Kapicioglu, David Rosenberg, Robert Schapire et al.
Recovering Distributions from Gaussian RKHS Embeddings
Motonobu Kanagawa, Kenji Fukumizu
A Level-set Hit-and-run Sampler for Quasi-Concave Distributions
Shane Jensen, Dean Foster
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani, Yuxin Chen, Amin Karbasi et al.
High-Dimensional Density Ratio Estimation with Extensions to Approximate Likelihood Computation
Rafael Izbicki, Ann Lee, Chad Schafer
Global Optimization Methods for Extended Fisher Discriminant Analysis
Satoru Iwata, Yuji Nakatsukasa, Akiko Takeda
A Finite-Sample Generalization Bound for Semiparametric Regression: Partially Linear Models
Ruitong Huang, Csaba Szepesvari
Latent Gaussian Models for Topic Modeling
Changwei Hu, Eunsu Ryu, David Carlson et al.
Tight Bounds for the Expected Risk of Linear Classifiers and PAC-Bayes Finite-Sample Guarantees
Jean Honorio, Tommi Jaakkola
Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors
Junya Honda, Akimichi Takemura
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
Matthew Hoffman, Bobak Shahriari, Nando Freitas
Tilted Variational Bayes
James Hensman, Max Zwiessele, Neil D. Lawrence
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Philipp Hennig, Søren Hauberg