Papers
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
Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Elvis Dohmatob et al.
Scalable Spike-and-Slab
Niloy Biswas, Lester Mackey, Xiao-Li Meng
Scaling Gaussian Process Optimization by Evaluating a Few Unique Candidates Multiple Times
Daniele Calandriello, Luigi Carratino, Alessandro Lazaric et al.
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika et al.
Scaling Structured Inference with Randomization
Yao Fu, John Cunningham, Mirella Lapata
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Jiahao Su, Wonmin Byeon, Furong Huang
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
Giorgio Giannone, Ole Winther
Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations
Jaehyeong Jo, Seul Lee, Sung Ju Hwang
Score-Guided Intermediate Level Optimization: Fast Langevin Mixing for Inverse Problems
Giannis Daras, Yuval Dagan, Alex Dimakis et al.
Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models
Paul Rolland, Volkan Cevher, Matthäus Kleindessner et al.
SDQ: Stochastic Differentiable Quantization with Mixed Precision
Xijie Huang, Zhiqiang Shen, Shichao Li et al.
SE(3) Equivariant Graph Neural Networks with Complete Local Frames
Weitao Du, He Zhang, Yuanqi Du et al.
Searching for BurgerFormer with Micro-Meso-Macro Space Design
Longxing Yang, Yu Hu, Shun Lu et al.
Secure Distributed Training at Scale
Eduard Gorbunov, Alexander Borzunov, Michael Diskin et al.
Secure Quantized Training for Deep Learning
Marcel Keller, Ke Sun