Papers
Revisiting Model Stitching to Compare Neural Representations
Yamini Bansal, Preetum Nakkiran, Boaz Barak
Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du et al.
Revisiting Smoothed Online Learning
Lijun Zhang, Wei Jiang, Shiyin Lu et al.
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders et al.
Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning
Milad Abdollahzadeh, Touba Malekzadeh, Ngai-Man (Man) Cheung
Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning
Chongjian GE, Youwei Liang, YIBING SONG et al.
Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
Weitong ZHANG, Dongruo Zhou, Quanquan Gu
Reward is enough for convex MDPs
Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins et al.
RIM: Reliable Influence-based Active Learning on Graphs
Wentao Zhang, Yexin Wang, Zhenbang You et al.
Risk-Averse Bayes-Adaptive Reinforcement Learning
Marc Rigter, Bruno Lacerda, Nick Hawes
Risk-averse Heteroscedastic Bayesian Optimization
Anastasia Makarova, Ilnura Usmanova, Ilija Bogunovic et al.
Risk-Aware Transfer in Reinforcement Learning using Successor Features
Michael Gimelfarb, Andre Barreto, Scott Sanner et al.
Risk Bounds and Calibration for a Smart Predict-then-Optimize Method
Heyuan Liu, Paul Grigas
Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures
Yuan Cao, Quanquan Gu, Mikhail Belkin
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Aurelien Bibaut, Nathan Kallus, Maria Dimakopoulou et al.
Risk Monotonicity in Statistical Learning
Zakaria Mhammedi
RL for Latent MDPs: Regret Guarantees and a Lower Bound
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis et al.
RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem
Eric Liang, Zhanghao Wu, Michael Luo et al.
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Wei Qiu, Xinrun Wang, Runsheng Yu et al.
RMM: Reinforced Memory Management for Class-Incremental Learning
Yaoyao Liu, Bernt Schiele, Qianru Sun
Robust Allocations with Diversity Constraints
Zeyu Shen, Lodewijk Gelauff, Ashish Goel et al.
Robust and Decomposable Average Precision for Image Retrieval
Elias Ramzi, Nicolas THOME, Clément Rambour et al.
Robust and differentially private mean estimation
Xiyang Liu, Weihao Kong, Sham Kakade et al.
Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems
Zixiu Wang, Yiwen Guo, Hu Ding
Robust Auction Design in the Auto-bidding World
Santiago Balseiro, Yuan Deng, Jieming Mao et al.