Papers
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Mattias Teye, Hossein Azizpour, Kevin Smith
Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane Wang, Zeb Kurth-Nelson et al.
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
Chao Tao, Saúl Blanco, Yuan Zhou
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Ashkan Norouzi-Fard, Jakub Tarnawski, Slobodan Mitrovic et al.
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
Yiping Lu, Aoxiao Zhong, Quanzheng Li et al.
Beyond the One-Step Greedy Approach in Reinforcement Learning
Yonathan Efroni, Gal Dalal, Bruno Scherrer et al.
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Luca Franceschi, Paolo Frasconi, Saverio Salzo et al.
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
Bowei Yan, Sanmi Koyejo, Kai Zhong et al.
Binary Partitions with Approximate Minimum Impurity
Eduardo Laber, Marco Molinaro, Felipe Mello Pereira
Black-box Adversarial Attacks with Limited Queries and Information
Andrew Ilyas, Logan Engstrom, Anish Athalye et al.
Black Box FDR
Wesley Tansey, Yixin Wang, David Blei et al.
Black-Box Variational Inference for Stochastic Differential Equations
Tom Ryder, Andrew Golightly, A. Stephen McGough et al.
Blind Justice: Fairness with Encrypted Sensitive Attributes
Niki Kilbertus, Adria Gascon, Matt Kusner et al.
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh, Efstratios Gavves, Max Welling
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Stefan Falkner, Aaron Klein, Frank Hutter
Born Again Neural Networks
Tommaso Furlanello, Zachary Lipton, Michael Tschannen et al.
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra, Christian Tjandraatmadja, Srikumar Ramalingam
Bounds on the Approximation Power of Feedforward Neural Networks
Mohammad Mehrabi, Aslan Tchamkerten, MANSOOR YOUSEFI
Bucket Renormalization for Approximate Inference
Sungsoo Ahn, Michael Chertkov, Adrian Weller et al.
Budgeted Experiment Design for Causal Structure Learning
AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash et al.
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Dong Yin, Yudong Chen, Ramchandran Kannan et al.
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Maithra Raghu, Alex Irpan, Jacob Andreas et al.
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
Lei Han, Yiheng Huang, Tong Zhang
Canonical Tensor Decomposition for Knowledge Base Completion
Timothee Lacroix, Nicolas Usunier, Guillaume Obozinski
Causal Bandits with Propagating Inference
Akihiro Yabe, Daisuke Hatano, Hanna Sumita et al.