Papers
Graph Sparsification Approaches for Laplacian Smoothing
Veeru Sadhanala, Yu-Xiang Wang, Ryan Tibshirani
A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees
Jean-Francis Roy, Mario Marchand, François Laviolette
Controlling Bias in Adaptive Data Analysis Using Information Theory
Daniel Russo, James Zou
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Hanie Sedghi, Majid Janzamin, Anima Anandkumar
On Searching for Generalized Instrumental Variables
Benito Zander, Maciej Liśkiewicz
Efficient Bregman Projections onto the Permutahedron and Related Polytopes
Cong Han Lim, Stephen J. Wright
Black-Box Policy Search with Probabilistic Programs
Jan-Willem Vandemeent, Brooks Paige, David Tolpin et al.
Online Learning with Noisy Side Observations
Tomáš Kocák, Gergely Neu, Michal Valko
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Unbounded Bayesian Optimization via Regularization
Bobak Shahriari, Alexandre Bouchard-Cote, Nando Freitas
Improper Deep Kernels
Uri Heinemann, Roi Livni, Elad Eban et al.
Unwrapping ADMM: Efficient Distributed Computing via Transpose Reduction
Tom Goldstein, Gavin Taylor, Kawika Barabin et al.
Consistently Estimating Markov Chains with Noisy Aggregate Data
Garrett Bernstein, Daniel Sheldon
Topic-Based Embeddings for Learning from Large Knowledge Graphs
Changwei Hu, Piyush Rai, Lawrence Carin
Non-negative Matrix Factorization for Discrete Data with Hierarchical Side-Information
Changwei Hu, Piyush Rai, Lawrence Carin
Scalable and Sound Low-Rank Tensor Learning
Hao Cheng, Yaoliang Yu, Xinhua Zhang et al.
Determinantal Regularization for Ensemble Variable Selection
Veronika Rockova, Gemma Moran, Edward George
Bethe Learning of Graphical Models via MAP Decoding
Kui Tang, Nicholas Ruozzi, David Belanger et al.
Tight Variational Bounds via Random Projections and I-Projections
Lun-Kai Hsu, Tudor Achim, Stefano Ermon
Bayesian Nonparametric Kernel-Learning
Junier B. Oliva, Avinava Dubey, Andrew G. Wilson et al.
Early Stopping as Nonparametric Variational Inference
David Duvenaud, Dougal Maclaurin, Ryan Adams
Fitting Spectral Decay with the k-Support Norm
Andrew McDonald, Massimiliano Pontil, Dimitris Stamos
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
Changyou Chen, David Carlson, Zhe Gan et al.
Inference for High-dimensional Exponential Family Graphical Models
Jialei Wang, Mladen Kolar
A Convex Surrogate Operator for General Non-Modular Loss Functions
Jiaqian Yu, Matthew Blaschko