Papers
Coordinated Multi-Agent Imitation Learning
Hoang M. Le, Yisong Yue, Peter Carr et al.
Coresets for Vector Summarization with Applications to Network Graphs
Dan Feldman, Sedat Ozer, Daniela Rus
Cost-Optimal Learning of Causal Graphs
Murat Kocaoglu, Alex Dimakis, Sriram Vishwanath
Count-Based Exploration with Neural Density Models
Georg Ostrovski, Marc G. Bellemare, Aäron Oord et al.
Counterfactual Data-Fusion for Online Reinforcement Learners
Andrew Forney, Judea Pearl, Elias Bareinboim
Coupling Distributed and Symbolic Execution for Natural Language Queries
Lili Mou, Zhengdong Lu, Hang Li et al.
Curiosity-driven Exploration by Self-supervised Prediction
Deepak Pathak, Pulkit Agrawal, Alexei A. Efros et al.
Dance Dance Convolution
Chris Donahue, Zachary C. Lipton, Julian McAuley
DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
Irina Higgins, Arka Pal, Andrei Rusu et al.
Data-Efficient Policy Evaluation Through Behavior Policy Search
Josiah P. Hanna, Philip S. Thomas, Peter Stone et al.
Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
Mason McGill, Pietro Perona
Decoupled Neural Interfaces using Synthetic Gradients
Max Jaderberg, Wojciech Marian Czarnecki, Simon Osindero et al.
DeepBach: a Steerable Model for Bach Chorales Generation
Gaëtan Hadjeres, François Pachet, Frank Nielsen
Deep Bayesian Active Learning with Image Data
Yarin Gal, Riashat Islam, Zoubin Ghahramani
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
Shayegan Omidshafiei, Jason Pazis, Christopher Amato et al.
Deep Generative Models for Relational Data with Side Information
Changwei Hu, Piyush Rai, Lawrence Carin
Deep IV: A Flexible Approach for Counterfactual Prediction
Jason Hartford, Greg Lewis, Kevin Leyton-Brown et al.
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC
Yulai Cong, Bo Chen, Hongwei Liu et al.
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon et al.
Deep Spectral Clustering Learning
Marc T. Law, Raquel Urtasun, Richard S. Zemel
Deep Tensor Convolution on Multicores
David Budden, Alexander Matveev, Shibani Santurkar et al.
Deep Transfer Learning with Joint Adaptation Networks
Mingsheng Long, Han Zhu, Jianmin Wang et al.
Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs
Michael Gygli, Mohammad Norouzi, Anelia Angelova
Deep Voice: Real-time Neural Text-to-Speech
Sercan Ö. Arık, Mike Chrzanowski, Adam Coates et al.
Deletion-Robust Submodular Maximization: Data Summarization with “the Right to be Forgotten”
Baharan Mirzasoleiman, Amin Karbasi, Andreas Krause