Papers
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Yue Yu, Jie Chen, Tian Gao et al.
Data Poisoning Attacks on Stochastic Bandits
Fang Liu, Ness Shroff
Data Shapley: Equitable Valuation of Data for Machine Learning
Amirata Ghorbani, James Zou
DBSCAN++: Towards fast and scalable density clustering
Jennifer Jang, Heinrich Jiang
Dead-ends and Secure Exploration in Reinforcement Learning
Mehdi Fatemi, Shikhar Sharma, Harm Van Seijen et al.
Decentralized Exploration in Multi-Armed Bandits
Raphael Feraud, Reda Alami, Romain Laroche
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
Anastasia Koloskova, Sebastian Stich, Martin Jaggi
Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models
Kaspar Märtens, Kieran Campbell, Christopher Yau
Deep Compressed Sensing
Yan Wu, Mihaela Rosca, Timothy Lillicrap
Deep Counterfactual Regret Minimization
Noam Brown, Adam Lerer, Sam Gross et al.
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.