Papers
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
Marco Baity-Jesi, Levent Sagun, Mario Geiger et al.
Comparison-Based Random Forests
Siavash Haghiri, Damien Garreau, Ulrike Luxburg
Competitive Caching with Machine Learned Advice
Thodoris Lykouris, Sergei Vassilvtiskii
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Xingyu Wang, Diego Klabjan
Compiling Combinatorial Prediction Games
Frederic Koriche
Composable Planning with Attributes
Amy Zhang, Sainbayar Sukhbaatar, Adam Lerer et al.
Composite Functional Gradient Learning of Generative Adversarial Models
Rie Johnson, Tong Zhang
Composite Marginal Likelihood Methods for Random Utility Models
Zhibing Zhao, Lirong Xia
Compressing Neural Networks using the Variational Information Bottleneck
Bin Dai, Chen Zhu, Baining Guo et al.
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm
Pavel Dvurechensky, Alexander Gasnikov, Alexey Kroshnin
Conditional Neural Processes
Marta Garnelo, Dan Rosenbaum, Christopher Maddison et al.
Conditional Noise-Contrastive Estimation of Unnormalised Models
Ciwan Ceylan, Michael U. Gutmann
Configurable Markov Decision Processes
Alberto Maria Metelli, Mirco Mutti, Marcello Restelli
Constant-Time Predictive Distributions for Gaussian Processes
Geoff Pleiss, Jacob Gardner, Kilian Weinberger et al.
Constrained Interacting Submodular Groupings
Andrew Cotter, Mahdi Milani Fard, Seungil You et al.
Constraining the Dynamics of Deep Probabilistic Models
Marco Lorenzi, Maurizio Filippone
ContextNet: Deep learning for Star Galaxy Classification
Noble Kennamer, David Kirkby, Alexander Ihler et al.
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Davide Bacciu, Federico Errica, Alessio Micheli
Continual Reinforcement Learning with Complex Synapses
Christos Kaplanis, Murray Shanahan, Claudia Clopath
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Pan Xu, Tianhao Wang, Quanquan Gu
Continuous-Time Flows for Efficient Inference and Density Estimation
Changyou Chen, Chunyuan Li, Liqun Chen et al.
Convergence guarantees for a class of non-convex and non-smooth optimization problems
Koulik Khamaru, Martin Wainwright
Convergent Tree Backup and Retrace with Function Approximation
Ahmed Touati, Pierre-Luc Bacon, Doina Precup et al.
Convolutional Imputation of Matrix Networks
Qingyun Sun, Mengyuan Yan, David Donoho et al.
Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Benjamin Van Roy