Papers
Regret Bounds for Learning State Representations in Reinforcement Learning
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric et al.
Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems
Young Hun Jung, Ambuj Tewari
Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function
Zihan Zhang, Xiangyang Ji
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel
Colin Wei, Jason Lee, Qiang Liu et al.
Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning
Wenjie Shi, Shiji Song, Hui Wu et al.
Regularized Gradient Boosting
Corinna Cortes, Mehryar Mohri, Dmitry Storcheus
Regularized Weighted Low Rank Approximation
Frank Ban, David Woodruff, Richard Zhang
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund et al.
Reinforcement Learning with Convex Constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daume III et al.
Reliable training and estimation of variance networks
Nicki Skafte, Martin Jørgensen, Søren Hauberg
REM: From Structural Entropy to Community Structure Deception
Yiwei Liu, Jiamou Liu, Zijian Zhang et al.
Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling
Ping Li, Xiaoyun Li, Cun-Hui Zhang
Residual Flows for Invertible Generative Modeling
Ricky T. Q. Chen, Jens Behrmann, David K. Duvenaud et al.
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
Bao Wang, Zuoqiang Shi, Stanley Osher
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks
Lixin Fan, Kam Woh Ng, Chee Seng Chan
Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach
Peilin Zhong, Yuchen Mo, Chang Xiao et al.
Rethinking Kernel Methods for Node Representation Learning on Graphs
Yu Tian, Long Zhao, Xi Peng et al.
Rethinking the CSC Model for Natural Images
Dror Simon, Michael Elad
Retrosynthesis Prediction with Conditional Graph Logic Network
Hanjun Dai, Chengtao Li, Connor Coley et al.
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Niru Maheswaranathan, Alex Williams, Matthew Golub et al.
Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness
Andrey Malinin, Mark Gales
Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs
Lorenzo Dall'Amico, Romain Couillet, Nicolas Tremblay
Riemannian batch normalization for SPD neural networks
Daniel Brooks, Olivier Schwander, Frederic Barbaresco et al.
Robust and Communication-Efficient Collaborative Learning
Amirhossein Reisizadeh, Hossein Taheri, Aryan Mokhtari et al.