Papers
Safe Policy Optimization with Local Generalized Linear Function Approximations
Akifumi Wachi, Yunyue Wei, Yanan Sui
Safe Pontryagin Differentiable Programming
Wanxin Jin, Shaoshuai Mou, George J. Pappas
Safe Reinforcement Learning by Imagining the Near Future
Garrett Thomas, Yuping Luo, Tengyu Ma
Safe Reinforcement Learning with Natural Language Constraints
Tsung-Yen Yang, Michael Y Hu, Yinlam Chow et al.
Sageflow: Robust Federated Learning against Both Stragglers and Adversaries
Jungwuk Park, Dong-Jun Han, Minseok Choi et al.
SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
Aaron Chan, Jiashu Xu, Boyuan Long et al.
Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons
Wenbo Ren, Jia Liu, Ness Shroff
Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
Maria-Florina F Balcan, Siddharth Prasad, Tuomas Sandholm et al.
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Yu Bai, Chi Jin, Huan Wang et al.
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
Bingyan Wang, Yuling Yan, Jianqing Fan
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting
Gen Li, Yuxin Chen, Yuejie Chi et al.
Sample Selection for Fair and Robust Training
Yuji Roh, Kangwook Lee, Steven Whang et al.
Sampling with Trusthworthy Constraints: A Variational Gradient Framework
Xingchao Liu, Xin Tong, Qiang Liu
Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?
Xiaolong Ma, Geng Yuan, Xuan Shen et al.
SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization
Amir Hertz, Or Perel, Raja Giryes et al.
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
Ziming Zhang, Yun Yue, Guojun Wu et al.
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Henry C Bendekgey, Erik B. Sudderth
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models
Kristopher Jensen, Ta-Chu Kao, Jasmine Stone et al.
Scalable Diverse Model Selection for Accessible Transfer Learning
Daniel Bolya, Rohit Mittapalli, Judy Hoffman
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
Arno Solin, Ella Tamir, Prakhar Verma
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields
Salar Fattahi, Andres Gomez
Scalable Intervention Target Estimation in Linear Models
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri et al.
Scalable Neural Data Server: A Data Recommender for Transfer Learning
Tianshi Cao, Sasha (Alexandre) Doubov, David Acuna et al.
Scalable Online Planning via Reinforcement Learning Fine-Tuning
Arnaud Fickinger, Hengyuan Hu, Brandon Amos et al.
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression
Ziyu Wang, Yuhao Zhou, Tongzheng Ren et al.