Papers
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
A Statistical Model for Event Sequence Data
Kevin Heins, Hal Stern
Exploiting the Limits of Structure Learning via Inherent Symmetry
Peng He, Changshui Zhang
Non-Asymptotic Analysis of Relational Learning with One Network
Peng He, Changshui Zhang
On Estimating Causal Effects based on Supplemental Variables
Takahiro Hayashi, Manabu Kuroki
Analytic Long-Term Forecasting with Periodic Gaussian Processes
Nooshin HajiGhassemi, Marc Deisenroth
Learning and Evaluation in Presence of Non-i.i.d. Label Noise
Nico Görnitz, Anne Porbadnigk, Alexander Binder et al.
Efficiently Enforcing Diversity in Multi-Output Structured Prediction
Abner Guzman-Rivera, Pushmeet Kohli, Dhruv Batra et al.
Bayesian Nonparametric Poisson Factorization for Recommendation Systems
Prem Gopalan, Francisco J. Ruiz, Rajesh Ranganath et al.
Robust Stochastic Principal Component Analysis
John Goes, Teng Zhang, Raman Arora et al.
Sketching the Support of a Probability Measure
Joachim Giesen, Soeren Laue, Lars Kuehne
Learning with Maximum A-Posteriori Perturbation Models
Andreea Gane, Tamir Hazan, Tommi Jaakkola
Fully-Automatic Bayesian Piecewise Sparse Linear Models
Riki Eto, Ryohei Fujimaki, Satoshi Morinaga et al.
Robust learning of inhomogeneous PMMs
Ralf Eggeling, Teemu Roos, Petri Myllymäki et al.
Bayesian Switching Interaction Analysis Under Uncertainty
Zoran Dzunic, John Fisher III
Efficient Inference for Complex Queries on Complex Distributions
Lili Dworkin, Michael Kearns, Lirong Xia
Avoiding pathologies in very deep networks
David Duvenaud, Oren Rippel, Ryan Adams et al.