Papers
Reinforcement Learning with Prototypical Representations
Denis Yarats, Rob Fergus, Alessandro Lazaric et al.
Relative Deviation Margin Bounds
Corinna Cortes, Mehryar Mohri, Ananda Theertha Suresh
Relative Positional Encoding for Transformers with Linear Complexity
Antoine Liutkus, Ondřej Cı́fka, Shih-Lun Wu et al.
REPAINT: Knowledge Transfer in Deep Reinforcement Learning
Yunzhe Tao, Sahika Genc, Jonathan Chung et al.
Representational aspects of depth and conditioning in normalizing flows
Frederic Koehler, Viraj Mehta, Andrej Risteski
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data
Esther Rolf, Theodora T Worledge, Benjamin Recht et al.
Representation Matters: Offline Pretraining for Sequential Decision Making
Mengjiao Yang, Ofir Nachum
Representation Subspace Distance for Domain Adaptation Regression
Xinyang Chen, Sinan Wang, Jianmin Wang et al.
Reserve Price Optimization for First Price Auctions in Display Advertising
Zhe Feng, Sebastien Lahaie, Jon Schneider et al.
Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica et al.
Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives
Da Xu, Chuanwei Ruan, Evren Korpeoglu et al.
Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss
Xue Yang, Junchi Yan, Qi Ming et al.
Re-understanding Finite-State Representations of Recurrent Policy Networks
Mohamad H Danesh, Anurag Koul, Alan Fern et al.
Revealing the Structure of Deep Neural Networks via Convex Duality
Tolga Ergen, Mert Pilanci
Revenue-Incentive Tradeoffs in Dynamic Reserve Pricing
Yuan Deng, Sebastien Lahaie, Vahab Mirrokni et al.
Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning
Tadashi Kozuno, Yunhao Tang, Mark Rowland et al.
Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline
Ankit Goyal, Hei Law, Bowei Liu et al.
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research
Johan Samir Obando Ceron, Pablo Samuel Castro
Reward Identification in Inverse Reinforcement Learning
Kuno Kim, Shivam Garg, Kirankumar Shiragur et al.
Riemannian Convex Potential Maps
Samuel Cohen, Brandon Amos, Yaron Lipman
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning
Yaqi Duan, Chi Jin, Zhiyuan Li
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach
Yingjie Fei, Zhuoran Yang, Zhaoran Wang
Rissanen Data Analysis: Examining Dataset Characteristics via Description Length
Ethan Perez, Douwe Kiela, Kyunghyun Cho
RNNRepair: Automatic RNN Repair via Model-based Analysis
Xiaofei Xie, Wenbo Guo, Lei Ma et al.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting
Soumyasundar Pal, Liheng Ma, Yingxue Zhang et al.