Papers
Learning privately from multiparty data
Jihun Hamm, Yingjun Cao, Mikhail Belkin
Learning Representations for Counterfactual Inference
Fredrik Johansson, Uri Shalit, David Sontag
Learning Simple Algorithms from Examples
Wojciech Zaremba, Tomas Mikolov, Armand Joulin et al.
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization
Eric Balkanski, Baharan Mirzasoleiman, Andreas Krause et al.
Learning to Filter with Predictive State Inference Machines
Wen Sun, Arun Venkatraman, Byron Boots et al.
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang
Linking losses for density ratio and class-probability estimation
Aditya Menon, Cheng Soon Ong
Loss factorization, weakly supervised learning and label noise robustness
Giorgio Patrini, Frank Nielsen, Richard Nock et al.
Low-Rank Matrix Approximation with Stability
Dongsheng Li, Chao Chen, Qin Lv et al.
Low-rank Solutions of Linear Matrix Equations via Procrustes Flow
Stephen Tu, Ross Boczar, Max Simchowitz et al.
Low-rank tensor completion: a Riemannian manifold preconditioning approach
Hiroyuki Kasai, Bamdev Mishra
Markov Latent Feature Models
Aonan Zhang, John Paisley
Markov-modulated Marked Poisson Processes for Check-in Data
Jiangwei Pan, Vinayak Rao, Pankaj Agarwal et al.
Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization
Zhiqiang Xu, Peilin Zhao, Jianneng Cao et al.
Metadata-conscious anonymous messaging
Giulia Fanti, Peter Kairouz, Sewoong Oh et al.
Meta–Gradient Boosted Decision Tree Model for Weight and Target Learning
Yury Ustinovskiy, Valentina Fedorova, Gleb Gusev et al.
Meta-Learning with Memory-Augmented Neural Networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick et al.
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz et al.
Minimizing the Maximal Loss: How and Why
Shai Shalev-Shwartz, Yonatan Wexler
Minimum Regret Search for Single- and Multi-Task Optimization
Jan Hendrik Metzen
Mixture Proportion Estimation via Kernel Embeddings of Distributions
Harish Ramaswamy, Clayton Scott, Ambuj Tewari
Model-Free Imitation Learning with Policy Optimization
Jonathan Ho, Jayesh Gupta, Stefano Ermon
Model-Free Trajectory Optimization for Reinforcement Learning
Riad Akrour, Gerhard Neumann, Hany Abdulsamad et al.
Multi-Bias Non-linear Activation in Deep Neural Networks
Hongyang Li, Wanli Ouyang, Xiaogang Wang