Papers
Deep Factors for Forecasting
Yuyang Wang, Alex Smola, Danielle Maddix et al.
Deep Gaussian Processes with Importance-Weighted Variational Inference
Hugh Salimbeni, Vincent Dutordoir, James Hensman et al.
Deep Generative Learning via Variational Gradient Flow
Yuan Gao, Yuling Jiao, Yang Wang et al.
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman et al.
DeepNose: Using artificial neural networks to represent the space of odorants
Ngoc Tran, Daniel Kepple, Sergey Shuvaev et al.
Deep Residual Output Layers for Neural Language Generation
Nikolaos Pappas, James Henderson
Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning
Dong Yin, Yudong Chen, Ramchandran Kannan et al.
Demystifying Dropout
Hongchang Gao, Jian Pei, Heng Huang
Diagnosing Bottlenecks in Deep Q-learning Algorithms
Justin Fu, Aviral Kumar, Matthew Soh et al.
Differentiable Dynamic Normalization for Learning Deep Representation
Ping Luo, Peng Zhanglin, Shao Wenqi et al.
Differentiable Linearized ADMM
Xingyu Xie, Jianlong Wu, Guangcan Liu et al.
Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory
Huizhuo Yuan, Yuren Zhou, Chris Junchi Li et al.
Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
Di Wang, Changyou Chen, Jinhui Xu
Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao et al.
Differentially Private Learning of Geometric Concepts
Haim Kaplan, Yishay Mansour, Yossi Matias et al.
Dimensionality Reduction for Tukey Regression
Kenneth Clarkson, Ruosong Wang, David Woodruff
Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning
Seungyul Han, Youngchul Sung
Direct Uncertainty Prediction for Medical Second Opinions
Maithra Raghu, Katy Blumer, Rory Sayres et al.
Dirichlet Simplex Nest and Geometric Inference
Mikhail Yurochkin, Aritra Guha, Yuekai Sun et al.
Discovering Conditionally Salient Features with Statistical Guarantees
Jaime Roquero Gimenez, James Zou
Discovering Context Effects from Raw Choice Data
Arjun Seshadri, Alex Peysakhovich, Johan Ugander
Discovering Latent Covariance Structures for Multiple Time Series
Anh Tong, Jaesik Choi
Discovering Options for Exploration by Minimizing Cover Time
Yuu Jinnai, Jee Won Park, David Abel et al.
Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography
Andrew Miller, Ziad Obermeyer, John Cunningham et al.