Papers
Equilibrated adaptive learning rates for non-convex optimization
Yann Dauphin, Harm de Vries, Yoshua Bengio
Learning Theory and Algorithms for Forecasting Non-stationary Time Series
Vitaly Kuznetsov, Mehryar Mohri
Convergence rates of sub-sampled Newton methods
Murat A Erdogdu, Andrea Montanari
Parallelizing MCMC with Random Partition Trees
Xiangyu Wang, Fangjian Guo, Katherine A. Heller et al.
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Eunho Yang, Aurelie C. Lozano
No-Regret Learning in Bayesian Games
Jason Hartline, Vasilis Syrgkanis, Eva Tardos
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
M-Best-Diverse Labelings for Submodular Energies and Beyond
Alexander Kirillov, Dmytro Shlezinger, Dmitry P Vetrov et al.
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods
Amir Dezfouli, Edwin V. Bonilla
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
Yunwen Lei, Urun Dogan, Alexander Binder et al.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction
Kisuk Lee, Aleksandar Zlateski, Vishwanathan Ashwin et al.
A Nonconvex Optimization Framework for Low Rank Matrix Estimation
Tuo Zhao, Zhaoran Wang, Han Liu
Information-theoretic lower bounds for convex optimization with erroneous oracles
Yaron Singer, Jan Vondrak
The Self-Normalized Estimator for Counterfactual Learning
Adith Swaminathan, Thorsten Joachims
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models
Juho Lee, Seungjin Choi
Streaming, Distributed Variational Inference for Bayesian Nonparametrics
Trevor Campbell, Julian Straub, John W. Fisher III et al.
Online Learning for Adversaries with Memory: Price of Past Mistakes
Oren Anava, Elad Hazan, Shie Mannor
Enforcing balance allows local supervised learning in spiking recurrent networks
Ralph Bourdoukan, Sophie Denève
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces
Takashi Takenouchi, Takafumi Kanamori
Semi-supervised Learning with Ladder Networks
Antti Rasmus, Mathias Berglund, Mikko Honkala et al.
Model-Based Relative Entropy Stochastic Search
Abbas Abdolmaleki, Rudolf Lioutikov, Jan R Peters et al.
Extending Gossip Algorithms to Distributed Estimation of U-statistics
Igor Colin, Aurélien Bellet, Joseph Salmon et al.
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman et al.
Super-Resolution Off the Grid
Qingqing Huang, Sham M. Kakade
Sparse Local Embeddings for Extreme Multi-label Classification
Kush Bhatia, Himanshu Jain, Purushottam Kar et al.