Optimization
Make Continual Learning Stronger via C-Flat
How to balance the learning'sensitivity-stability' upon new task training and
Breaking Long-Tailed Learning Bottlenecks: A Controllable Paradigm with Hypernetwork-Generated Diverse Experts
We generate a set of diverse expert models via hypernetworks to cover all possible distribution scenarios, and optimize the model ensemble to adapt to any test distribution. Crucially, in any distribution scenario, we can flexibly output a dedicated model solution that matches the user's preference.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Intent learning, which aims to learn users' intents for user understanding and
Continuous Partitioning for Graph-Based Semi-Supervised Learning
This thresholding can further exacerbate the aforementioned degeneracy.
Optimizing over Multiple Distributions under Generalized Quasar-Convexity Condition
We study a typical optimization model where the optimization variable is composed of multiple probability distributions. Though the model appears frequently in practice, such as for policy problems, it lacks specific analysis in the general setting.
