state-of-the-art algorithms
State-of-the-art algorithms refer to the most advanced and effective models or methods available in a specific domain at a given time, often used as benchmarks for comparison in research.
- Beyond Average Value Function in Precision Medicine: Maximum Probability-Driven Reinforcement Learning for Survival Analysis
- Doubly Robust Alignment for Large Language Models
- Improving LLM General Preference Alignment via Optimistic Online Mirror Descent
- MOBO-OSD: Batch Multi-Objective Bayesian Optimization via Orthogonal Search Directions
- Non-stationary Equivariant Graph Neural Networks for Physical Dynamics Simulation
- Offline imitation learning in $Q^\pi$-realizable MDPs without expert realizability
- URB - Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles