Papers
Provable Alternating Gradient Descent for Non-negative Matrix Factorization with Strong Correlations
Yuanzhi Li, Yingyu Liang
Dropout Inference in Bayesian Neural Networks with Alpha-divergences
Yingzhen Li, Yarin Gal
Learning to Align the Source Code to the Compiled Object Code
Dor Levy, Lior Wolf
Doubly Greedy Primal-Dual Coordinate Descent for Sparse Empirical Risk Minimization
Qi Lei, Ian En-Hsu Yen, Chao-yuan Wu et al.
Deriving Neural Architectures from Sequence and Graph Kernels
Tao Lei, Wengong Jin, Regina Barzilay et al.
Confident Multiple Choice Learning
Kimin Lee, Changho Hwang, KyoungSoo Park et al.
Bayesian inference on random simple graphs with power law degree distributions
Juho Lee, Creighton Heaukulani, Zoubin Ghahramani et al.
Coordinated Multi-Agent Imitation Learning
Hoang M. Le, Yisong Yue, Peter Carr et al.
Deep Spectral Clustering Learning
Marc T. Law, Raquel Urtasun, Richard S. Zemel
Consistent k-Clustering
Silvio Lattanzi, Sergei Vassilvitskii
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui Lan, Sebastian Pokutta, Yi Zhou et al.
Co-clustering through Optimal Transport
Charlotte Laclau, Ievgen Redko, Basarab Matei et al.
Grammar Variational Autoencoder
Matt J. Kusner, Brooks Paige, José Miguel Hernández-Lobato
Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things
Ashish Kumar, Saurabh Goyal, Manik Varma
Evaluating Bayesian Models with Posterior Dispersion Indices
Alp Kucukelbir, Yixin Wang, David M. Blei
Active Learning for Cost-Sensitive Classification
Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang et al.
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Alexander Kolesnikov, Christoph H. Lampert
Sub-sampled Cubic Regularization for Non-convex Optimization
Jonas Moritz Kohler, Aurelien Lucchi
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh, Percy Liang
Cost-Optimal Learning of Causal Graphs
Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath
SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization
Juyong Kim, Yookoon Park, Gunhee Kim et al.
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Taeksoo Kim, Moonsu Cha, Hyunsoo Kim et al.
Graph-based Isometry Invariant Representation Learning
Renata Khasanova, Pascal Frossard
On Approximation Guarantees for Greedy Low Rank Optimization
Rajiv Khanna, Ethan R. Elenberg, Alexandros G. Dimakis et al.
Meritocratic Fairness for Cross-Population Selection
Michael Kearns, Aaron Roth, Zhiwei Steven Wu