multi-objective-learning

This paper proposes a multi-objective neural basis model (MONBM) framework to simultaneously optimize accuracy, interpretability, and fairness in generalized additive neural networks, revealing complex trade-offs between these trustworthiness dimensions.

This paper proposes a regularity-aware stochastic multi-gradient descent method (MoRe) that adaptively switches between conflict-avoidant and scalarization updates. The method achieves improved convergence rates from O~T^{-1/4} to O~T^{-1/2} in nonconvex settings while maintaining per-iterate conflict avoidance.