Papers
Prediction and Control with Temporal Segment Models
Nikhil Mishra, Pieter Abbeel, Igor Mordatch
Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”
Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause
Tight Bounds for Approximate Carathéodory and Beyond
Vahab Mirrokni, Renato Paes Leme, Adrian Vladu et al.
Device Placement Optimization with Reinforcement Learning
Azalia Mirhoseini, Hieu Pham, Quoc V. Le et al.
Variational Boosting: Iteratively Refining Posterior Approximations
Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
Discovering Discrete Latent Topics with Neural Variational Inference
Yishu Miao, Edward Grefenstette, Phil Blunsom
Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections
Zakaria Mhammedi, Andrew Hellicar, Ashfaqur Rahman et al.
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Lars Mescheder, Sebastian Nowozin, Andreas Geiger
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates
Jiali Mei, Yohann De Castro, Yannig Goude et al.
Risk Bounds for Transferring Representations With and Without Fine-Tuning
Daniel McNamara, Maria-Florina Balcan
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
Mason McGill, Pietro Perona
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Lucas Maystre, Matthias Grossglauser
Just Sort It! A Simple and Effective Approach to Active Preference Learning
Lucas Maystre, Matthias Grossglauser
Bayesian Models of Data Streams with Hierarchical Power Priors
Andrés Masegosa, Thomas D. Nielsen, Helge Langseth et al.
On Mixed Memberships and Symmetric Nonnegative Matrix Factorizations
Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Global optimization of Lipschitz functions
Cédric Malherbe, Nicolas Vayatis
Frame-based Data Factorizations
Sebastian Mair, Ahcène Boubekki, Ulf Brefeld
A Laplacian Framework for Option Discovery in Reinforcement Learning
Marlos C. Machado, Marc G. Bellemare, Michael Bowling
Interactive Learning from Policy-Dependent Human Feedback
James MacGlashan, Mark K. Ho, Robert Loftin et al.
Self-Paced Co-training
Fan Ma, Deyu Meng, Qi Xie et al.
Stochastic Gradient MCMC Methods for Hidden Markov Models
Yi-An Ma, Nicholas J. Foti, Emily B. Fox
Learning Gradient Descent: Better Generalization and Longer Horizons
Kaifeng Lv, Shunhua Jiang, Jian Li