Papers
Robust Unsupervised Learning via L-statistic Minimization
Andreas Maurer, Daniela Angela Parletta, Andrea Paudice et al.
RRL: Resnet as representation for Reinforcement Learning
Rutav M Shah, Vikash Kumar
Run-Sort-ReRun: Escaping Batch Size Limitations in Sliced Wasserstein Generative Models
Jose Lezama, Wei Chen, Qiang Qiu
Safe Reinforcement Learning Using Advantage-Based Intervention
Nolan C Wagener, Byron Boots, Ching-An Cheng
Safe Reinforcement Learning with Linear Function Approximation
Sanae Amani, Christos Thrampoulidis, Lin Yang
SagaNet: A Small Sample Gated Network for Pediatric Cancer Diagnosis
Yuhan Liu, Shiliang Sun
SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning
Lokesh Chandra Das, Myounggyu Won
Sample Complexity of Robust Linear Classification on Separated Data
Robi Bhattacharjee, Somesh Jha, Kamalika Chaudhuri
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
Dhruv Malik, Aldo Pacchiano, Vishwak Srinivasan et al.
Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network
Zhibin Duan, Dongsheng Wang, Bo Chen et al.
Scalable Certified Segmentation via Randomized Smoothing
Marc Fischer, Maximilian Baader, Martin Vechev
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Jiaojiao Fan, Amirhossein Taghvaei, Yongxin Chen
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Joel Z Leibo, Edgar A Dueñez-Guzman, Alexander Vezhnevets et al.
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Alexander Immer, Matthias Bauer, Vincent Fortuin et al.
Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš, Stephan Günnemann
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Gasteiger, Marten Lienen, Stephan Günnemann
Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Gordon Gordon Wilson et al.
Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing
Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman et al.
Scaling Properties of Deep Residual Networks
Alain-Sam Cohen, Rama Cont, Alain Rossier et al.
Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
Chao Jia, Yinfei Yang, Ye Xia et al.
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
Xiangjun Wang, Junxiao Song, Penghui Qi et al.
SECANT: Self-Expert Cloning for Zero-Shot Generalization of Visual Policies
Linxi Fan, Guanzhi Wang, De-An Huang et al.
Segmenting Hybrid Trajectories using Latent ODEs
Ruian Shi, Quaid Morris
Selecting Data Augmentation for Simulating Interventions
Maximilian Ilse, Jakub M Tomczak, Patrick Forré