Papers
Distributed Learning with Sublinear Communication
Jayadev Acharya, Chris De Sa, Dylan Foster et al.
Distributed Low-rank Matrix Factorization With Exact Consensus
Zhihui Zhu, Qiuwei Li, Xinshuo Yang et al.
Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets
Mehrdad Ghadiri, Mark Schmidt
Distributed Representation of Words in Cause and Effect Spaces
Zhipeng Xie, Feiteng Mu
Distributed Weighted Matching via Randomized Composable Coresets
Sepehr Assadi, Mohammadhossein Bateni, Vahab Mirrokni
DISTRIBUTIONAL CONCAVITY REGULARIZATION FOR GANS
Shoichiro Yamaguchi, Masanori Koyama
Distributionally Adversarial Attack
Tianhang Zheng, Changyou Chen, Kui Ren
Distributionally Robust Language Modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto et al.
Distributionally Robust Language Modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto et al.
Distributionally Robust Optimization and Generalization in Kernel Methods
Matthew Staib, Stefanie Jegelka
Distributionally Robust Semi-Supervised Learning for People-Centric Sensing
Kaixuan Chen, Lina Yao, Dalin Zhang et al.
Distributionally Robust Submodular Maximization
Matthew Staib, Bryan Wilder, Stefanie Jegelka
Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN
Dror Freirich, Tzahi Shimkin, Ron Meir et al.
Distributional Policy Optimization: An Alternative Approach for Continuous Control
Chen Tessler, Guy Tennenholtz, Shie Mannor
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Hengshuai Yao, Linglong Kong et al.
Distributional reinforcement learning with linear function approximation
Marc G. Bellemare, Nicolas Le Roux, Pablo Samuel Castro et al.
Distributional Reward Decomposition for Reinforcement Learning
Zichuan Lin, Li Zhao, Derek Yang et al.
Distributional Semantics Meets Construction Grammar. towards a Unified Usage-Based Model of Grammar and Meaning
Giulia Rambelli, Emmanuele Chersoni, Philippe Blache et al.
Distributional Semantics Meets Multi-Label Learning
Vivek Gupta, Rahul Wadbude, Nagarajan Natarajan et al.
Distribution-Based Semi-Supervised Learning for Activity Recognition
Hangwei Qian, Sinno Jialin Pan, Chunyan Miao
Distribution calibration for regression
Hao Song, Tom Diethe, Meelis Kull et al.
Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning
Wenbin Li, Jinglin Xu, Jing Huo et al.
Distribution-Dependent Analysis of Gibbs-ERM Principle
Ilja Kuzborskij, Nicolò Cesa-Bianchi, Csaba Szepesvári
Distribution-Independent PAC Learning of Halfspaces with Massart Noise
Ilias Diakonikolas, Themis Gouleakis, Christos Tzamos