Papers
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
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