Papers
Extreme Stochastic Variational Inference: Distributed Inference for Large Scale Mixture Models
Jiong Zhang, Parameswaran Raman, Shihao Ji et al.
Fast Algorithms for Sparse Reduced-Rank Regression
Benjamin Dubois, Jean-François Delmas, Guillaume Obozinski
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron
Sharan Vaswani, Francis Bach, Mark Schmidt
Fast and Robust Shortest Paths on Manifolds Learned from Data
Georgios Arvanitidis, Soren Hauberg, Philipp Hennig et al.
Faster First-Order Methods for Stochastic Non-Convex Optimization on Riemannian Manifolds
Pan Zhou, Xiao-Tong Yuan, Jiashi Feng
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs
Philippe Wenk, Alkis Gotovos, Stefan Bauer et al.
Fast Stochastic Algorithms for Low-rank and Nonsmooth Matrix Problems
Dan Garber, Atara Kaplan
Feature subset selection for the multinomial logit model via mixed-integer optimization
Shunsuke Kamiya, Ryuhei Miyashiro, Yuichi Takano
Finding the bandit in a graph: Sequential search-and-stop
Pierre Perrault, Vianney Perchet, Michal Valko
Fisher Information and Natural Gradient Learning in Random Deep Networks
Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin et al.
Fixing Mini-batch Sequences with Hierarchical Robust Partitioning
Shengjie Wang, Wenruo Bai, Chandrashekhar Lavania et al.
Forward Amortized Inference for Likelihood-Free Variational Marginalization
Luca Ambrogioni, Umut Güçlü, Julia Berezutskaya et al.
Foundations of Sequence-to-Sequence Modeling for Time Series
Zelda Mariet, Vitaly Kuznetsov
From Cost-Sensitive Classification to Tight F-measure Bounds
Kevin Bascol, Rémi Emonet, Elisa Fromont et al.
Gain estimation of linear dynamical systems using Thompson Sampling
Matias I. Müller, Cristian R. Rojas
Gaussian Process Latent Variable Alignment Learning
Ieva Kazlauskaite, Carl Henrik Ek, Neill Campbell
Gaussian Process Modulated Cox Processes under Linear Inequality Constraints
Andrés F. Lopez-Lopera, ST John, Nicolas Durrande
Gaussian Regression with Convex Constraints
Matey Neykov
Generalized Boltzmann Machine with Deep Neural Structure
Yingru Liu, Dongliang Xie, Xin Wang
Generalizing the theory of cooperative inference
Pei Wang, Pushpi Paranamana, Patrick Shafto
Globally-convergent Iteratively Reweighted Least Squares for Robust Regression Problems
Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain et al.
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira
Graph to Graph: a Topology Aware Approach for Graph Structures Learning and Generation
Mingming Sun, Ping Li