Papers
Robust Multi-Objective Bayesian Optimization Under Input Noise
Samuel Daulton, Sait Cakmak, Maximilian Balandat et al.
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
Tianyu Pang, Min Lin, Xiao Yang et al.
Robustness Implies Generalization via Data-Dependent Generalization Bounds
Kenji Kawaguchi, Zhun Deng, Kyle Luh et al.
Robustness in Multi-Objective Submodular Optimization: a Quantile Approach
Cedric Malherbe, Kevin Scaman
Robustness Verification for Contrastive Learning
Zekai Wang, Weiwei Liu
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie, Shagun Sodhani, Chelsea Finn et al.
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning
Lorenz Richter, Julius Berner
Robust Task Representations for Offline Meta-Reinforcement Learning via Contrastive Learning
Haoqi Yuan, Zongqing Lu
Robust Training of Neural Networks Using Scale Invariant Architectures
Zhiyuan Li, Srinadh Bhojanapalli, Manzil Zaheer et al.
Robust Training under Label Noise by Over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu et al.
ROCK: Causal Inference Principles for Reasoning about Commonsense Causality
Jiayao Zhang, Hongming Zhang, Weijie Su et al.
Role-based Multiplex Network Embedding
Hegui Zhang, Gang Kou
Rotting Infinitely Many-Armed Bandits
Jung-Hun Kim, Milan Vojnovic, Se-Young Yun
RUMs from Head-to-Head Contests
Matteo Almanza, Flavio Chierichetti, Ravi Kumar et al.
Safe Exploration for Efficient Policy Evaluation and Comparison
Runzhe Wan, Branislav Kveton, Rui Song
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
Martino Bernasconi, Federico Cacciamani, Matteo Castiglioni et al.
Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis
Ziyi Chen, Yi Zhou, Rong-Rong Chen et al.
Sample Efficient Learning of Predictors that Complement Humans
Mohammad-Amin Charusaie, Hussein Mozannar, David Sontag et al.
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao, Ming Yin, Ming Min et al.
Sanity Simulations for Saliency Methods
Joon Sik Kim, Gregory Plumb, Ameet Talwalkar
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation
Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee et al.
Scalable Computation of Causal Bounds
Madhumitha Shridharan, Garud Iyengar
Scalable Deep Gaussian Markov Random Fields for General Graphs
Joel Oskarsson, Per Sidén, Fredrik Lindsten
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games
Mathieu Lauriere, Sarah Perrin, Sertan Girgin et al.
Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation
Sebastian E Ament, Carla P Gomes