scalable algorithms
Algorithms designed to efficiently handle increases in input size or dimensionality, maintaining or improving performance without a proportional increase in resource requirements.
- BO4Mob: Bayesian Optimization Benchmarks for High-Dimensional Urban Mobility Problem
- Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics
- Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping
- Partial Correlation Network Estimation by Semismooth Newton Methods
- Robust and Scalable Autonomous Reinforcement Learning in Irreversible Environments
- Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity
- Semi-supervised Vertex Hunting, with Applications in Network and Text Analysis