Papers
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar, Aditya Grover, Stefano Ermon
Variational Network Inference: Strong and Stable with Concrete Support
Amir Dezfouli, Edwin Bonilla, Richard Nock
Accurate Inference for Adaptive Linear Models
Yash Deshpande, Lester Mackey, Vasilis Syrgkanis et al.
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, Jose-Miguel Hernandez-Lobato, Finale Doshi-Velez et al.
Stochastic Video Generation with a Learned Prior
Emily Denton, Rob Fergus
Minibatch Gibbs Sampling on Large Graphical Models
Chris De Sa, Vincent Chen, Wing Wong
Escaping Saddles with Stochastic Gradients
Hadi Daneshmand, Jonas Kohler, Aurelien Lucchi et al.
Asynchronous Byzantine Machine Learning (the case of SGD)
Georgios Damaskinos, El-Mahdi El-Mhamdi, Rachid Guerraoui et al.
Compressing Neural Networks using the Variational Information Bottleneck
Bin Dai, Chen Zhu, Baining Guo et al.
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Bo Dai, Albert Shaw, Lihong Li et al.
Adversarial Attack on Graph Structured Data
Hanjun Dai, Hui Li, Tian Tian et al.
Learning Steady-States of Iterative Algorithms over Graphs
Hanjun Dai, Zornitsa Kozareva, Bo Dai et al.
Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver et al.
Mix & Match Agent Curricula for Reinforcement Learning
Wojciech Czarnecki, Siddhant Jayakumar, Max Jaderberg et al.
Inference Suboptimality in Variational Autoencoders
Chris Cremer, Xuechen Li, David Duvenaud
Constrained Interacting Submodular Groupings
Andrew Cotter, Mahdi Milani Fard, Seungil You et al.
Online Learning with Abstention
Corinna Cortes, Giulia DeSalvo, Claudio Gentile et al.
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Dane Corneil, Wulfram Gerstner, Johanni Brea
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Cédric Colas, Olivier Sigaud, Pierre-Yves Oudeyer
Online Linear Quadratic Control
Alon Cohen, Avinatan Hasidim, Tomer Koren et al.
On Acceleration with Noise-Corrupted Gradients
Michael Cohen, Jelena Diakonikolas, Lorenzo Orecchia
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
John Co-Reyes, YuXuan Liu, Abhishek Gupta et al.
Stochastic Wasserstein Barycenters
Sebastian Claici, Edward Chien, Justin Solomon
An Iterative, Sketching-based Framework for Ridge Regression
Agniva Chowdhury, Jiasen Yang, Petros Drineas
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Yinlam Chow, Ofir Nachum, Mohammad Ghavamzadeh