theoretical convergence
Theoretical convergence pertains to the rigorous mathematical proofs that demonstrate that a given optimization algorithm will converge to a solution under specific conditions, providing foundational knowledge for understanding the behavior of learning algorithms.
- A Difference-of-Convex Functions Approach to Energy-Based Iterative Reasoning
- Can Class-Priors Help Single-Positive Multi-Label Learning?
- Conformal Prediction in The Loop: A Feedback-Based Uncertainty Model for Trajectory Optimization
- Efficient Last-Iterate Convergence in Solving Extensive-Form Games
- MGUP: A Momentum-Gradient Alignment Update Policy for Stochastic Optimization
- Unlocker: Disentangle the Deadlock of Learning between Label-noisy and Long-tailed Data