Papers
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Jinghui Chen, Pan Xu, Lingxiao Wang et al.
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Kevin Tian, Teng Zhang, James Zou
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Wissam Siblini, Pascale Kuntz, Frank Meyer
Crowdsourcing with Arbitrary Adversaries
Matthaeus Kleindessner, Pranjal Awasthi
CRVI: Convex Relaxation for Variational Inference
Ghazal Fazelnia, John Paisley
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Daphna Weinshall, Gad Cohen, Dan Amir
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Hugo Raguet, Loic Landrieu
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman, Eric Tzeng, Taesung Park et al.
Data-Dependent Stability of Stochastic Gradient Descent
Ilja Kuzborskij, Christoph Lampert
Data Summarization at Scale: A Two-Stage Submodular Approach
Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam et al.
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Qiang Qiu, Xiuyuan Cheng, Calderbank et al.
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez et al.
Decoupled Parallel Backpropagation with Convergence Guarantee
Zhouyuan Huo, Bin Gu, Yang et al.
Decoupling Gradient-Like Learning Rules from Representations
Philip Thomas, Christoph Dann, Emma Brunskill
Deep Asymmetric Multi-task Feature Learning
Hae Beom Lee, Eunho Yang, Sung Ju Hwang
Deep Bayesian Nonparametric Tracking
Aonan Zhang, John Paisley
Deep Density Destructors
David Inouye, Pradeep Ravikumar
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Junru Wu, Yue Wang, Zhenyu Wu et al.
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
Thomas Laurent, James Brecht
Deep Models of Interactions Across Sets
Jason Hartford, Devon Graham, Kevin Leyton-Brown et al.
Deep One-Class Classification
Lukas Ruff, Robert Vandermeulen, Nico Goernitz et al.
Deep Predictive Coding Network for Object Recognition
Haiguang Wen, Kuan Han, Junxing Shi et al.
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Kyowoon Lee, Sol-A Kim, Jaesik Choi et al.
Deep Variational Reinforcement Learning for POMDPs
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le et al.