Papers
REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis
Yu-Shao Peng, Kai-Fu Tang, Hsuan-Tien Lin et al.
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Zi Wang, Beomjoon Kim, Leslie Pack Kaelbling
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
Shinji Ito, Daisuke Hatano, Hanna Sumita et al.
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
Sarah Dean, Horia Mania, Nikolai Matni et al.
Regularization Learning Networks: Deep Learning for Tabular Datasets
Ira Shavitt, Eran Segal
Regularizing by the Variance of the Activations' Sample-Variances
Etai Littwin, Lior Wolf
Reinforced Continual Learning
Ju Xu, Zhanxing Zhu
Reinforcement Learning for Solving the Vehicle Routing Problem
MohammadReza Nazari, Afshin Oroojlooy, Lawrence Snyder et al.
Reinforcement Learning of Theorem Proving
Cezary Kaliszyk, Josef Urban, Henryk Michalewski et al.
Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee
Relating Leverage Scores and Density using Regularized Christoffel Functions
Edouard Pauwels, Francis Bach, Jean-Philippe Vert
Relational recurrent neural networks
Adam Santoro, Ryan Faulkner, David Raposo et al.
Removing Hidden Confounding by Experimental Grounding
Nathan Kallus, Aahlad Manas Puli, Uri Shalit
Removing the Feature Correlation Effect of Multiplicative Noise
Zijun Zhang, Yining Zhang, Zongpeng Li
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban et al.
Reparameterization Gradient for Non-differentiable Models
Wonyeol Lee, Hangyeol Yu, Hongseok Yang
Representation Balancing MDPs for Off-policy Policy Evaluation
Yao Liu, Omer Gottesman, Aniruddh Raghu et al.
Representation Learning for Treatment Effect Estimation from Observational Data
Liuyi Yao, Sheng Li, Yaliang Li et al.
Representation Learning of Compositional Data
Marta Avalos, Richard Nock, Cheng Soon Ong et al.
Representer Point Selection for Explaining Deep Neural Networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen et al.
ResNet with one-neuron hidden layers is a Universal Approximator
Hongzhou Lin, Stefanie Jegelka
Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
Junqi Tang, Mohammad Golbabaee, Francis Bach et al.
RetGK: Graph Kernels based on Return Probabilities of Random Walks
Zhen Zhang, Mianzhi Wang, Yijian Xiang et al.
Reversible Recurrent Neural Networks
Matthew MacKay, Paul Vicol, Jimmy Ba et al.
Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
Sofiane Dhouib, Ievgen Redko