Papers
Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo et al.
Fast Distributed k-Center Clustering with Outliers on Massive Data
Gustavo Malkomes, Matt J Kusner, Wenlin Chen et al.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Shaoqing Ren, Kaiming He, Ross Girshick et al.
Fast Lifted MAP Inference via Partitioning
Somdeb Sarkhel, Parag Singla, Vibhav G Gogate
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms
Rasmus Kyng, Anup Rao, Sushant Sachdeva
Fast Randomized Kernel Ridge Regression with Statistical Guarantees
Ahmed Alaoui, Michael W. Mahoney
Fast Rates for Exp-concave Empirical Risk Minimization
Tomer Koren, Kfir Levy
Fast Second Order Stochastic Backpropagation for Variational Inference
Kai Fan, Ziteng Wang, Jeff Beck et al.
Fast Two-Sample Testing with Analytic Representations of Probability Measures
Kacper P Chwialkowski, Aaditya Ramdas, Dino Sejdinovic et al.
Fighting Bandits with a New Kind of Smoothness
Jacob D. Abernethy, Chansoo Lee, Ambuj Tewari
Finite-Time Analysis of Projected Langevin Monte Carlo
Sebastien Bubeck, Ronen Eldan, Joseph Lehec
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial
David I Inouye, Pradeep K Ravikumar, Inderjit S Dhillon
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees
François-Xavier Briol, Chris Oates, Mark Girolami et al.
From random walks to distances on unweighted graphs
Tatsunori Hashimoto, Yi Sun, Tommi Jaakkola
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning
Jiajun Wu, Ilker Yildirim, Joseph J. Lim et al.
GAP Safe screening rules for sparse multi-task and multi-class models
Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort et al.
Gaussian Process Random Fields
David Moore, Stuart Russell
Generalization in Adaptive Data Analysis and Holdout Reuse
Cynthia Dwork, Vitaly Feldman, Moritz Hardt et al.
Generative Image Modeling Using Spatial LSTMs
Lucas Theis, Matthias Bethge
GP Kernels for Cross-Spectrum Analysis
Kyle R Ulrich, David E Carlson, Kafui Dzirasa et al.
Gradient Estimation Using Stochastic Computation Graphs
John Schulman, Nicolas Heess, Theophane Weber et al.
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone et al.
Grammar as a Foreign Language
Oriol Vinyals, Łukasz Kaiser, Terry Koo et al.
Halting in Random Walk Kernels
Mahito Sugiyama, Karsten Borgwardt
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks
Minhyung Cho, Chandra Dhir, Jaehyung Lee