Optimization
Safe and Efficient: A Primal-Dual Method for Offline Convex CMDPs under Partial Data Coverage
Offline safe reinforcement learning (RL) aims to find an optimal policy using a pre-collected dataset when data collection is impractical or risky.
Efficiently avoiding saddle points with zero order methods: No gradients required
Emmanouil-Vasileios Vlatakis-Gkaragkounis, Lampros Flokas, Georgios Piliouras
Neural Information Processing Systems http://nips.cc/
Möbius Transformation for Fast Inner Product Search on Graph
Zhixin Zhou, Shulong Tan, Zhaozhuo Xu, Ping Li
Neural Information Processing Systems http://nips.cc/
Average Individual Fairness: Algorithms, Generalization and Experiments
Saeed Sharifi-Malvajerdi, Michael Kearns, Aaron Roth
Neural Information Processing Systems http://nips.cc/
db8e1af0cb3aca1ae2d0018624204529-Paper.pdf
Federated learning (FL) has gain growing interests for its capability of learning from distributed data sources collectively without the need of accessing the raw data samples across different sources.
